Systems and methods for retraining a model a target variable in a tiered framework

ABSTRACT

A method for operating an industrial automation system may involve receiving, via a first module of a plurality of modules in a control system, an indication that an error between a measurement associated with a target variable that corresponds with at least a portion of the industrial automation system and a modeled value for the target variable. The method may then involve determining, via the first module, whether the error is within a first range of values and retraining a model used to generate the modeled value for the target variable based on a portion of a plurality of sets of data points acquired via a plurality of sensors disposed in the industrial automation system in response to the error being within the first range of values.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is related to U.S. Application Ser. No. 16/146,116entitled “SYSTEMS AND METHODS FOR ENCRYPTING DATA BETWEEN MODULES OF ACONTROL SYSTEM,” filed Sep. 28, 2018, U.S. Application Ser. No.16/146,647 entitled “SYSTEMS AND METHODS FOR LOCALLY MODELING A TARGETVARIABLE,” filed Sep. 28, 2018, and U.S. Application No. 16,146,664entitled “SYSTEMS AND METHODS FOR CONTROLLING INDUSTRIAL DEVICES BASEDON MODELED TARGET VARIABLES,” filed Sep. 28, 2018. Each of these relatedapplications are herein incorporated by reference in their entireties.

BACKGROUND

The present disclosure generally relates to control systems and, moreparticularly, to using control system for monitoring, diagnostics,and/or modeling generation.

Generally, a control system may facilitate performance of an industrialautomation process by controlling operation of one or more automationdevices. For example, to facilitate performing an industrial automationprocess, the control system may determine a control action and instructan automation device (e.g., a rod-pump) to perform the control action.Additionally, the control system may facilitate monitoring performanceof the process to determine whether the process is operating as desired.When not operating as desired, the control system may also facilitateperforming diagnostics on the process to determine cause of undesiredoperation.

As complexity (e.g., number of automation devices and/or amount ofprocess data) of a process increases, complexity of monitoring and/ordiagnostics may also increase. For example, increasing amount of processdata determined from the industrial automation system may increasenumber of factors to consider when determining whether the process isoperating as desired and/or the cause of undesired operation. In otherwords, monitoring and/or diagnostics for a complex process may bedependent on ability to efficiency process large amounts of data,particularly when performed in real-time or near real-time (e.g., onlineduring operation of the process). As such, it may be desirable toprovide improved systems and methods for analyzing the process dataacquired from the industrial automation system in real time or near realtime to efficiently monitor the performance of the industrial automationsystem and increase the efficiency in which the industrial automationsystem operates.

This section is intended to introduce the reader to various aspects ofart that may be related to various aspects of the present techniques,which are described and/or claimed below. This discussion is believed tobe helpful in providing the reader with background information tofacilitate a better understanding of the various aspects of the presentdisclosure. Accordingly, it should be understood that these statementsare to be read in this light, and not as admissions of prior art.

BRIEF DESCRIPTION

A summary of certain embodiments disclosed herein is set forth below. Itshould be understood that these aspects are presented merely to providethe reader with a brief summary of these certain embodiments and thatthese aspects are not intended to limit the scope of this disclosure.Indeed, this disclosure may encompass a variety of aspects that may notbe set forth below.

In one embodiment, an industrial automation system may include anautomation device and a plurality of sensors that may monitor aplurality of properties associated with the automation device. Eachsensor of the plurality of sensors may acquire a set of data pointsassociated with a respective property of the plurality of properties.The industrial automation system may also include a control systemcommunicatively coupled to the plurality of sensors, such that thecontrol system may include a first module of a plurality of modules. Thefirst module may receive a plurality of sets of data points acquired bythe plurality of sensors, determine a modeled value of one of theplurality of sets of data points based on a portion of the plurality ofsets of data points and a model, and determine whether an error betweenthe modeled value of the one of the plurality of sets of data points isgreater than a first threshold. The first module may then retrain themodel associated with the one of the plurality of sets of data pointsbased on the portion of the plurality of sets of data points in responseto the error being greater than the first threshold.

In another embodiment, a method for operating an industrial automationsystem may involve receiving, via a first module of a plurality ofmodules in a control system, an indication that an error between ameasurement associated with a target variable that corresponds with atleast a portion of the industrial automation system and a modeled valuefor the target variable. The method may then involve determining, viathe first module, whether the error is within a first range of valuesand retraining a model used to generate the modeled value for the targetvariable based on a portion of a plurality of sets of data pointsacquired via a plurality of sensors disposed in the industrialautomation system in response to the error being within the first rangeof values.

In yet another embodiment, a non-transitory computer-readable medium mayinclude computer-executable instructions that, when executed, may causea processor to receive a plurality of sets of data points acquired by aplurality of sensors, determine a modeled value of one of the pluralityof sets of data points based on a portion of the plurality of sets ofdata points and a model, and determine whether an error between themodeled value of the one of the plurality of sets of data points isgreater than a first threshold. The instructions may then cause theprocessor to retrain the model associated with the one of the pluralityof sets of data points based on the portion of the plurality of sets ofdata points in response to the error being greater than the firstthreshold.

DRAWINGS

These and other features, aspects, and advantages of the presentdisclosure will become better understood when the following detaileddescription is read with reference to the accompanying drawings in whichlike characters represent like parts throughout the drawings, wherein:

FIG. 1 illustrates an example industrial automation system employed by afood manufacturer, in accordance with an embodiment;

FIG. 2 illustrates a diagrammatical representation of an exemplarycontrol and monitoring system that may be employed in any suitableindustrial automation system, in accordance with an embodiment;

FIG. 3 illustrates example components that may be part of acontrol/monitoring device in a control system for the industrialautomation system, in accordance with an embodiment;

FIG. 4 illustrates an example control system with a number of modules,in accordance with an embodiment;

FIG. 5 is a block diagram representative of the control system of FIG.4, in accordance with an embodiment;

FIG. 6 illustrates a communication network that routes requests forinformation or data through a routing system to a local control system,in accordance with an embodiment;

FIG. 7 illustrates a flow chart of a method for controlling operationsof the industrial automation equipment using a control system that islocally connected to datasets regarding the industrial automationequipment, in accordance with an embodiment;

FIG. 8 illustrates a flow chart of a method for encrypting data outputby the local control system, in accordance with an embodiment;

FIG. 9 describes an example encryption process that may be employed by amodule of the local control system, in accordance with an embodiment;

FIG. 10 illustrates a flow chart of a method for decrypting theencrypted information determined using the method of FIG. 9, inaccordance with an embodiment;

FIG. 11 illustrates a flow chart of a method for identifying targetvariables for an artificial intelligence (AI) module to model, inaccordance with an embodiment;

FIG. 12 illustrates a multi-dimensional graph including five identifiedclusters, in accordance with an embodiment;

FIG. 13 illustrates a flow chart of a method for identifying the targetvariables of a particular cluster of data points, in accordance with anembodiment;

FIG. 14 illustrates an example directed graph, in accordance with anembodiment;

FIG. 15 illustrates an example pruned directed graph, in accordance withan embodiment;

FIG. 16 illustrates an example directed graph that depicts relationshipproperties between data points, in accordance with an embodiment;

FIG. 17 illustrates a flowchart of a method determining strength ofrelationships between data points, in accordance with an embodiment;

FIG. 18 illustrates a flowchart of a method for plotting data points ininformational space, in accordance with an embodiment;

FIG. 19 illustrates a sample graph of position, torque, and velocityvalues aligned with respect to sequence, in accordance with anembodiment;

FIG. 20 illustrates an informational space map, in accordance with anembodiment;

FIG. 21 illustrates an example set of parameters that may define atarget variable, in accordance with an embodiment;

FIG. 22 illustrates a flow chart of a method for adjusting operations ofan industrial automation device based on modeling operations, inaccordance with an embodiment;

FIG. 23 illustrates an example set of waveforms that may define a targetvariable, in accordance with an embodiment; and

FIG. 24 illustrates a flow chart of a method for retraining a model fora target variable, in accordance with an embodiment.

DETAILED DESCRIPTION

One or more specific embodiments of the present disclosure will bedescribed below. In an effort to provide a concise description of theseembodiments, all features of an actual implementation may not bedescribed in the specification. It should be appreciated that in thedevelopment of any such actual implementation, as in any engineering ordesign project, numerous implementation-specific decisions must be madeto achieve the developers' specific goals, such as compliance withsystem-related and business-related constraints, which may vary from oneimplementation to another. Moreover, it should be appreciated that sucha development effort might be complex and time consuming, but wouldnevertheless be a routine undertaking of design, fabrication, andmanufacture for those of ordinary skill having the benefit of thisdisclosure.

When introducing elements of various embodiments of the presentdisclosure, the articles “a,” “an,” “the,” and “said” are intended tomean that there are one or more of the elements. The terms “comprising,”“including,” and “having” are intended to be inclusive and mean thatthere may be additional elements other than the listed elements.

As discussed above, a control system may control operation of one ormore automation devices to facilitate performing an industrialautomation process. Industrial automation processes may be used invarious contexts, such as a manufacturing plant, a resource extractionsystem, a hydrocarbon extraction site, a chemical refinery facility, anindustrial plant, a power generation system, a mining system, a brewery,or the like. For example, in a resource extraction system context, acontrol system may control load and position of a rod pump (e.g., anautomation device) to perform an oil extraction process. Althoughexamples are provided with regard to specific contexts, one of ordinaryskill in the art will recognize that these examples are not intended tobe limiting and that the techniques described herein can be used withany suitable context.

To improve operation, the control system may monitor performance of theone or more automation devices and/or the industrial automation processas a whole. For example, the control system may determine whetheroperation is as desired by analyzing process data. As used herein,“process data” is intended to describe data indicative of operation ofan industrial automation process. For example, the process data mayinclude inputs to the industrial automation process, outputs from theindustrial automation process, disturbance variables (e.g.,environmental conditions), constraints on operation, operationalparameters (e.g., temperature, speed, load, position, voltage, and/orpressure) of an automation device, and the like.

Additionally, the control system may perform diagnostics to facilitateidentifying cause of undesired operation and remedying the undesiredoperation. For example, the control system may analyze the process datato determine a likely cause of undesired operation and possible steps toremedy the likely cause. As such, the control system may analyze theprocess data to facilitate performance monitoring and/or diagnostics.

When monitoring performance and/or performance diagnostics in real-timeor near-real time (e.g., online during operation of the industrialautomation process), the control system may be limited with regard tocomputing resources and time that may be allocated to analyze theprocess data. Moreover, the amount of process data analyzed to monitorperformance and/or perform diagnostics generally increases withcomplexity of the industrial automation process. For example, sinceprocess data may include operation parameters of automation devices, theamount of process data may increase as number of automation devices usedin the industrial automation process increases. Additionally, sinceprocess may data may include environmental conditions, the amount ofprocess data may increase as distribution size of automation devicesused in the industrial automation process increases.

With the foregoing in mind, as the process data is collected regularly(e.g., time-series data), the collected data may be transmitted to anonsite server, a cloud-computing system, a cloud-based service, or thelike. Traditionally, the onsite server or the cloud-computing systemwould leverage its processing power to enable an expert (e.g. a datascientist) to efficiently analyze and interpret the data (e.g.,time-series data) due to the amount of data collected in real-time.However, as the volume and velocity of the data collected fromindustrial automation systems, it is now recognized that theinfrastructure and cost associated with this approach may not be anefficient use of resources. For example, as cloud-computing systems havebeen used more frequently, the additional costs, the complex logistics,and the security concerns with regard to using cloud-computing systemsto analyze and store process data have made the cloud-based analysisless attractive. By way of example, when assessing problems andidentifying solutions to the problems in an industrial automationsystem, analysis of data pertaining to a particular part or portion ofthe industrial automation system may be performed effectively with alocal controller or control system without the use of the processingpower of the cloud-computing system and without the presence of thehuman expert (e.g. a data scientist) in the online work flow of dataanalysis. Indeed, controllers and/or computing modules that are locallypresent at the portion of the industrial automation system may haveaccess to the information that is used to respond to a request forinformation, and may be able to provide an answer for the request basedon data that is locally available without the computing resources of acloud-computing device.

Accordingly, as will be described in more detail below, the presentdisclosure provides techniques to improve efficiency of performancemonitoring and/or diagnostics, for example, to facilitate performance inreal-time or near real-time using local controllers or control systems.In some embodiments, a local control system or controller may haveaccess to data that may enable the local control system to determineroot causes for detected issues (e.g., alarms), determine preventativeactions to avoid certain undesirable operating conditions, performcertain types of analysis, and the like. As discussed above, since thelocal control system may perform these types of analysis without thecomputing resources of a cloud-computing device, the results of theanalysis may be communicated to other control systems or computingdevices without transmitting the data outside a local area network thatconnects to other components of the industrial automation system. Thatis, the analyzed data may be communicated to other components in theindustrial automation system without sending the data to the cloud oroutside the bounds created by the local area network of the industrialautomation system.

In some embodiments, the local control system may be made up of a numberof modules or components that perform various operations with respect tocontrolling the operations of an industrial automation device or thelike. For example, the local control system may include an input/output(I/O) module that facilitates the communication of data between sensorsand other control systems, a controller module that controls theoperation of the respective industrial automation device, an artificialintelligence (AI) module that performs certain types of analysis usingmodels, algorithms, machine-learning techniques, and the like. Thecomponents part of the local control system may communicate with eachother via a communication backplane or the like.

The AI module may, in some embodiments, receive a request forinformation that may involve determining a state of the industrialautomation device, a root cause for a problem associated with theindustrial automation device, or the like. Using the data available tothe AI module via the modules of the local control system, the AI modulemay perform the requested analysis automatically without receiving dataunavailable to the local control system. Moreover, the AI module mayperform the analysis using the computing resources of the AI modulewithout transmitting data outside of the local control system foranalysis. In this way, the AI module may efficiently perform therequested analysis without compromising the integrity of the data bycommunicating the data outside of the local area network.

To enable the automated analysis of the real-time control system data,the self-driving AI module disclosed herein may be equipped with anauto-monitoring capability where the output of the AI module (e.g.prediction of a target variable) is compared to the actual output (e.g.real-time measurements of the target variable) and a measure of outputquality is created. If an acceptable measure of quality is not reachedafter some time (e.g., defined by the user for example), the AI modulewill inform the automation system (e.g., control system) that theanalysis of the available data does not yield an acceptable answer tothe requested information and therefore further steps must be taken. Forexample, the user can decide to check available data for validity ormake additional data available to the AI module if possible. The AImodule may also include the intelligence to expand the domain of dataincluded for analysis. This capability may assist in optimizing thecommunication bandwidth in an automation/control system. For example, ina control system with several thousand variables (e.g., tags), it maynot be prudent to consider all these tags for analysis at one time. Assuch, the AI module may include the intelligence to examine selectedsubsets of the available variables in a methodical manner and retainonly the variables that it finds as most effective (e.g., resulting inmovement to desired change) for the analysis.

To ensure that the data communicated between local components of a localcontrol system or between components of the local area network in theindustrial automation system is secure, certain security measures may byundertaken to prevent potential hackers from accessing the data. Forexample, the local control system may encrypt data communicated betweenlocal components connected via the communication backplane. Forinstance, the controller module of the local control system may interactwith the other local components in a secure manner by encrypting datacommunicated there between. In this way, the data communicated betweencomponents may be protected from snooping, tampering, or modifyingactions that may be performed by entities trying to gain access to thedata. Additional details with regard to facilitating the communicationbetween the components of the local control system will be discussedbelow with reference to FIGS. 1-24.

By way of introduction, FIG. 1 illustrates an example industrialautomation system 10 employed by a food manufacturer. It should be notedthat although the example industrial automation system 10 of FIG. 1 isdirected at a food manufacturer, the present embodiments describedherein may be employed within any suitable industry, such as automotive,mining, hydrocarbon production, manufacturing, and the like. Thefollowing brief description of the example industrial automation system10 employed by the food manufacturer is provided herein to helpfacilitate a more comprehensive understanding of how the embodimentsdescribed herein may be applied to industrial devices to significantlyimprove the operations of the respective industrial automation system.As such, the embodiments described herein should not be limited to beapplied to the example depicted in FIG. 1.

Referring now to FIG. 1, the example industrial automation system 10 fora food manufacturer may include silos 12 and tanks 14. The silos 12 andthe tanks 14 may store different types of raw material, such as grains,salt, yeast, sweeteners, flavoring agents, coloring agents, vitamins,minerals, and preservatives. In some embodiments, sensors 16 may bepositioned within or around the silos 12, the tanks 14, or othersuitable locations within the industrial automation system 10 to measurecertain properties, such as temperature, mass, volume, pressure,humidity, and the like.

The raw materials may be provided to a mixer 18, which may mix the rawmaterials together according to a specified ratio. The mixer 18 andother machines in the industrial automation system 10 may employ certainindustrial automation devices 20 to control the operations of the mixer18 and other machines. The industrial automation devices 20 may includecontrollers, input/output (I/O) modules, motor control centers, motors,human machine interfaces (HMIs), operator interfaces, contactors,starters, sensors 16, actuators, conveyors, drives, relays, protectiondevices, switchgear, compressors, sensor, actuator, firewall, networkswitches (e.g., Ethernet switches, modular-managed, fixed-managed,service-router, industrial, unmanaged, etc.) and the like.

The mixer 18 may provide a mixed compound to a depositor 22, which maydeposit a certain amount of the mixed compound onto conveyor 24. Thedepositor 22 may deposit the mixed compound on the conveyor 24 accordingto a shape and amount that may be specified to a control system for thedepositor 22. The conveyor 24 may be any suitable conveyor system thattransports items to various types of machinery across the industrialautomation system 10. For example, the conveyor 24 may transportdeposited material from the depositor 22 to an oven 26, which may bakethe deposited material. The baked material may be transported to acooling tunnel 28 to cool the baked material, such that the cooledmaterial may be transported to a tray loader 30 via the conveyor 24. Thetray loader 30 may include machinery that receives a certain amount ofthe cooled material for packaging. By way of example, the tray loader 30may receive 25 ounces of the cooled material, which may correspond to anamount of cereal provided in a cereal box.

A tray wrapper 32 may receive a collected amount of cooled material fromthe tray loader 30 into a bag, which may be sealed. The tray wrapper 32may receive the collected amount of cooled material in a bag and sealthe bag using appropriate machinery. The conveyor 24 may transport thebagged material to case packer 34, which may package the bagged materialinto a box. The boxes may be transported to a palletizer 36, which maystack a certain number of boxes on a pallet that may be lifted using aforklift or the like. The stacked boxes may then be transported to ashrink wrapper 38, which may wrap the stacked boxes with shrink-wrap tokeep the stacked boxes together while on the pallet. The shrink-wrappedboxes may then be transported to storage or the like via a forklift orother suitable transport vehicle.

To perform the operations of each of the devices in the exampleindustrial automation system 10, the industrial automation devices 20may be used to provide power to the machinery used to perform certaintasks, provide protection to the machinery from electrical surges,prevent injuries from occurring with human operators in the industrialautomation system 10, monitor the operations of the respective device,communicate data regarding the respective device to a supervisorycontrol system 40, and the like. In some embodiments, each industrialautomation device 20 or a group of industrial automation devices 20 maybe controlled using a local control system 42. The local control system42 may include receive data regarding the operation of the respectiveindustrial automation device 20, other industrial automation devices 20,user inputs, and other suitable inputs to control the operations of therespective industrial automation device(s) 20.

By way of example, FIG. 2 illustrates a diagrammatical representation ofan exemplary control and monitoring system 50 that may be employed inany suitable industrial automation system 10, in accordance withembodiments presented herein. In FIG. 2, the control and monitoringsystem 50 is illustrated as including a human machine interface (HMI) 52and a control/monitoring device 54 or automation controller adapted tointerface with devices that may monitor and control various types ofindustrial automation equipment 56. By way of example, the industrialautomation equipment 56 may include the mixer 18, the depositor 22, theconveyor 24, the oven 26, and the other pieces of machinery described inFIG. 1.

It should be noted that the HMI 52 and the control/monitoring device 54,in accordance with embodiments of the present techniques, may befacilitated by the use of certain network strategies. Indeed, anindustry standard network may be employed, such as DeviceNet, to enabledata transfer. Such networks permit the exchange of data in accordancewith a predefined protocol, and may provide power for operation ofnetworked elements.

As discussed above, the industrial automation equipment 56 may take manyforms and include devices for accomplishing many different and variedpurposes. For example, the industrial automation equipment 56 mayinclude machinery used to perform various operations in a compressorstation, an oil refinery, a batch operation for making food items, amechanized assembly line, and so forth. Accordingly, the industrialautomation equipment 56 may comprise a variety of operationalcomponents, such as electric motors, valves, actuators, temperatureelements, pressure sensors, or a myriad of machinery or devices used formanufacturing, processing, material handling, and other applications.

Additionally, the industrial automation equipment 56 may include varioustypes of equipment that may be used to perform the various operationsthat may be part of an industrial application. For instance, theindustrial automation equipment 56 may include electrical equipment,hydraulic equipment, compressed air equipment, steam equipment,mechanical tools, protective equipment, refrigeration equipment, powerlines, hydraulic lines, steam lines, and the like. Some example types ofequipment may include mixers, machine conveyors, tanks, skids,specialized original equipment manufacturer machines, and the like. Inaddition to the equipment described above, the industrial automationequipment 56 may be made up of certain automation devices 20, which mayinclude controllers, input/output (I/O) modules, motor control centers,motors, human machine interfaces (HMIs), operator interfaces,contactors, starters, sensors 16, actuators, drives, relays, protectiondevices, switchgear, compressors, firewall, network switches (e.g.,Ethernet switches, modular-managed, fixed-managed, service-router,industrial, unmanaged, etc.) and the like.

In certain embodiments, one or more properties of the industrialautomation equipment 56 may be monitored and controlled by certainequipment for regulating control variables used to operate theindustrial automation equipment 56. For example, the sensors 16 andactuators 60 may monitor various properties of the industrial automationequipment 56 and may adjust operations of the industrial automationequipment 56, respectively.

In some cases, the industrial automation equipment 56 may be associatedwith devices used by other equipment. For instance, scanners, gauges,valves, flow meters, and the like may be disposed on industrialautomation equipment 56. Here, the industrial automation equipment 56may receive data from the associated devices and use the data to performtheir respective operations more efficiently. For example, a controller(e.g., control/monitoring device 54) of a motor drive may receive dataregarding a temperature of a connected motor and may adjust operationsof the motor drive based on the data.

In certain embodiments, the industrial automation equipment 56 mayinclude a communication component that enables the industrial equipment56 to communicate data between each other and other devices. Thecommunication component may include a network interface that may enablethe industrial automation equipment 56 to communicate via variousprotocols such as Ethernet/IP®, ControlNet®, DeviceNet®, or any otherindustrial communication network protocol. Alternatively, thecommunication component may enable the industrial automation equipment56 to communicate via various wired or wireless communication protocols,such as Wi-Fi, mobile telecommunications technology (e.g., 2G, 3G, 4G,LTE), Bluetooth®, near-field communications technology, and the like.

The sensors 16 may be any number of devices adapted to provideinformation regarding process conditions. The actuators 60 may includeany number of devices adapted to perform a mechanical action in responseto a signal from a controller (e.g., the control/monitoring device 54).The sensors 16 and actuators 60 may be utilized to operate theindustrial automation equipment 56. Indeed, they may be utilized withinprocess loops that are monitored and controlled by thecontrol/monitoring device 54 and/or the HMI 52. Such a process loop maybe activated based on process inputs (e.g., input from a sensor 16) ordirect operator input received through the HMI 52. As illustrated, thesensors 16 and actuators 60 are in communication with thecontrol/monitoring device 54. Further, the sensors 16 and actuators 60may be assigned a particular address in the control/monitoring device 54and receive power from the control/monitoring device 54 or attachedmodules.

Input/output (I/O) modules 62 may be added or removed from the controland monitoring system 50 via expansion slots, bays or other suitablemechanisms. In certain embodiments, the I/O modules 62 may be includedto add functionality to the control/monitoring device 54, or toaccommodate additional process features. For instance, the I/O modules62 may communicate with new sensors 16 or actuators 60 added to monitorand control the industrial automation equipment 56. It should be notedthat the I/O modules 62 may communicate directly to sensors 16 oractuators 60 through hardwired connections or may communicate throughwired or wireless sensor networks, such as Hart or IOLink.

Generally, the I/O modules 62 serve as an electrical interface to thecontrol/monitoring device 54 and may be located proximate or remote fromthe control/monitoring device 54, including remote network interfaces toassociated systems. In such embodiments, data may be communicated withremote modules over a common communication link, or network, whereinmodules on the network communicate via a standard communicationsprotocol. Many industrial controllers can communicate via networktechnologies such as Ethernet (e.g., IEEE802.3, TCP/IP, UDP,Ethernet/IP, and so forth), ControlNet, DeviceNet or other networkprotocols (Foundation Fieldbus (H1 and Fast Ethernet) Modbus TCP,Profibus) and also communicate to higher level computing systems.

In the illustrated embodiment, several of the I/O modules 62 maytransfer input and output signals between the control/monitoring device54 and the industrial automation equipment 56. As illustrated, thesensors 16 and actuators 60 may communicate with the control/monitoringdevice 54 via one or more of the I/O modules 62 coupled to thecontrol/monitoring device 54.

In certain embodiments, the control/monitoring system 50 (e.g., the HMI52, the control/monitoring device 54, the sensors 16, the actuators 60,the I/O modules 62) and the industrial automation equipment 56 may makeup an industrial automation application 64. The industrial automationapplication 64 may involve any type of industrial process or system usedto manufacture, produce, process, or package various types of items. Forexample, the industrial applications 64 may include industries such asmaterial handling, packaging industries, manufacturing, processing,batch processing, the example industrial automation system 10 of FIG. 1,and the like.

In certain embodiments, the control/monitoring device 54 may becommunicatively coupled to a computing device 66 and a cloud-basedcomputing system 68. In this network, input and output signals generatedfrom the control/monitoring device 54 may be communicated between thecomputing device 66 and the cloud-based computing system 68. Althoughthe control/monitoring device 54 may be capable of communicating withthe computing device 66 and the cloud-based computing system 68, asmentioned above, in certain embodiments, the control/monitoring device54 (e.g., local computing system 42) may perform certain operations andanalysis without sending data to the computing device 66 or thecloud-based computing system 68.

In any case, FIG. 3 illustrates example components that may be part ofthe control/monitoring device 54, in accordance with embodimentspresented herein. For example, the control/monitoring device 54 mayinclude a communication component 72, a processor 74, a memory 76, astorage 78, input/output (I/O) ports 80, an image sensor 82 (e.g., acamera), a location sensor 84, a display 86, additional sensors (e.g.,vibration sensors, temperature sensors), and the like. The communicationcomponent 72 may be a wireless or wired communication component that mayfacilitate communication between the industrial automation equipment 56,the cloud-based computing system 68, and other communication capabledevices.

The processor 74 may be any type of computer processor or microprocessorcapable of executing computer-executable code. The processor 74 may alsoinclude multiple processors that may perform the operations describedbelow. The memory 76 and the storage 78 may be any suitable articles ofmanufacture that can serve as media to store processor-executable code,data, or the like. These articles of manufacture may representcomputer-readable media (e.g., any suitable form of memory or storage)that may store the processor-executable code used by the processor 74 toperform the presently disclosed techniques. Generally, the processor 74may execute software applications that include programs that enable auser to track and/or monitor operations of the industrial automationequipment 56 via a local or remote communication link. That is, thesoftware applications may communicate with the control/monitoring device54 and gather information associated with the industrial automationequipment 56 as determined by the control/monitoring device 54, via thesensors 16 disposed on the industrial automation equipment 56 and thelike.

The memory 76 and the storage 78 may also be used to store the data,analysis of the data, the software applications, and the like. Thememory 76 and the storage 78 may represent non-transitorycomputer-readable media (e.g., any suitable form of memory or storage)that may store the processor-executable code used by the processor 74 toperform various techniques described herein. It should be noted thatnon-transitory merely indicates that the media is tangible and not asignal.

In one embodiment, the memory 76 and/or storage 78 may include asoftware application that may be executed by the processor 74 and may beused to monitor, control, access, or view one of the industrialautomation equipment 56. As such, the computing device 66 maycommunicatively couple to industrial automation equipment 56 or to arespective computing device of the industrial automation equipment 56via a direct connection between the devices or via the cloud-basedcomputing system 58. The software application may perform variousfunctionalities, such as track statistics of the industrial automationequipment 56, store reasons for placing the industrial automationequipment 56 offline, determine reasons for placing the industrialautomation equipment 56 offline, secure industrial automation equipment56 that is offline, deny access to place an offline industrialautomation equipment 56 back online until certain conditions are met,and so forth.

The I/O ports 80 may be interfaces that may couple to other peripheralcomponents such as input devices (e.g., keyboard, mouse), sensors,input/output (I/O) modules, and the like. I/O modules may enable thecomputing device 66 or other control/monitoring devices 54 tocommunicate with the industrial automation equipment 56 or other devicesin the industrial automation system via the I/O modules.

The image sensor 82 may include any image acquisition circuitry such asa digital camera capable of acquiring digital images, digital videos, orthe like. The location sensor 84 may include circuitry designed todetermine a physical location of the computing device 66. In oneembodiment, the location sensor 84 may include a global positioningsystem (GPS) sensor that acquires GPS coordinates for thecontrol/monitoring device 54.

The display 86 may depict visualizations associated with software orexecutable code being processed by the processor 74. In one embodiment,the display 86 may be a touch display capable of receiving inputs (e.g.,parameter data for operating the industrial automation equipment 56)from a user of the control/monitoring device 54. As such, the display 86may serve as a user interface to communicate with the industrialautomation equipment 56. The display 86 may be used to display agraphical user interface (GUI) for operating the industrial automationequipment 56, for tracking the maintenance of the industrial automationequipment 56, and the like. The display 86 may be any suitable type ofdisplay, such as a liquid crystal display (LCD), plasma display, or anorganic light emitting diode (OLED) display, for example. Additionally,in one embodiment, the display 86 may be provided in conjunction with atouch-sensitive mechanism (e.g., a touch screen) that may function aspart of a control interface for the industrial automation equipment 56or for a number of pieces of industrial automation equipment in theindustrial automation application 64, to control the general operationsof the industrial automation application 64. In some embodiments, theoperator interface may be characterized as the HMI 52, a human-interfacemachine, or the like.

Although the components described above have been discussed with regardto the control/monitoring device 54, it should be noted that similarcomponents may make up other computing devices described herein.Further, it should be noted that the listed components are provided asexample components and the embodiments described herein are not to belimited to the components described with reference to FIG. 3.

Referring back to FIG. 2, in operation, the industrial automationapplication 64 may receive one or more inputs used to produce one ormore outputs. For example, the inputs may include feedstock, electricalenergy, fuel, parts, assemblies, sub-assemblies, operational parameters(e.g., sensor measurements), or any combination thereof. Additionally,the outputs may include finished products, semi-finished products,assemblies, manufacturing products, by products, or any combinationthereof.

To produce the one or more outputs, the control/monitoring device 54 maycontrol operation of the industrial automation application 64. In someembodiments, the control/monitoring device 54 may control operation byoutputting control signals to instruct industrial automation equipment56 to perform a control action by implementing manipulated variable setpoints. For example, the control/monitoring device 54 may instruct amotor (e.g., an automation device 20) to implement a control action byactuating at a particular speed (e.g., a manipulated variable setpoint).

In some embodiments, the control/monitoring device 54 may determine themanipulated variable set points based at least in part on process data.As described above, the process data may be indicative of operation ofthe industrial automation device 20, the industrial automation equipment56, the industrial automation application 64, and the like. As such, theprocess data may include operational parameters of the industrialautomation device 20 and/or operational parameters of the industrialautomation application 65. For example, the operational parameters mayinclude any suitable type, such as temperature, flow rate, electricalpower, and the like.

Thus, the control/monitoring device 54 may receive process data from oneor more of the industrial automation devices 20, the sensors 16, or thelike. In some embodiments, the sensor 16 may determine an operationalparameter and communicate a measurement signal indicating theoperational parameter to the control/monitoring device 54. For example,a temperature sensor may measure temperature of a motor (e.g., anautomation device 20) and transmit a measurement signal indicating themeasured temperature to the control/monitoring device 54. Thecontrol/monitoring device 54 may then analyze the process data tomonitor performance of the industrial automation application 64 (e.g.,determine an expected operational state) and/or perform diagnostics onthe industrial automation application 64.

To facilitate controlling operation and/or performing other functions,the control/monitoring device 54 may include one or more controllers,such as one or more model predictive control (MPC) controllers, one ormore proportional-integral-derivative (PID) controllers, one or moreneural network controllers, one or more fuzzy logic controllers, or anycombination thereof.

In some embodiments, the supervisory control system 40 may providecentralized control over operation of the industrial automationapplication 64. For example, the supervisory control system 40 mayenable centralized communication with a user (e.g., operator). Tofacilitate, the supervisory control system 40 may include the display 86to facilitate providing information to the user. For example, thedisplay 86 may display visual representations of information, such asprocess data, selected features, expected operational parameters, and/orrelationships there between. Additionally, the supervisory controlsystem 40 may include similar components as the control/monitoringdevice 54 described above in FIG. 3.

On the other hand, the control/monitoring device 54 may providelocalized control over a portion of the industrial automationapplication 64 via the local control system 42. For example, in thedepicted embodiment of FIG. 1, the local control system 42 that may bepart of the mixer 18 may provide control over operation of a firstautomation device 20 that controls the mixer 18, and a second localcontrol system 42 may provide control over operation of a secondautomation device 20 that controls the operation of the depositor 22.

In some embodiments, the local control system 42 may control operationof a portion of the industrial automation application 64 based at leastin part on the control strategy determined by the supervisory controlsystem 40. Additionally, the supervisory control system 40 may determinethe control strategy based at least in part on process data determinedby the local control system 42. Thus, to implement the control strategy,the supervisory control system 40 and the local control systems 42 maybe communicatively coupled via a network, which may be any suitabletype, such as an Ethernet/IP network, a ControlNet network, a DeviceNetnetwork, a Data Highway Plus network, a Remote I/O network, a FoundationFieldbus network, a Serial, DH-485 network, a SynchLink network, or anycombination thereof.

It should be appreciated that the described embodiment of the localcontrol system 42 is merely intended to be illustrative and notlimiting. The local control system 42 may include one or more componentsof the control/monitoring device 54. In some cases, thecontrol/monitoring device 54 may be one component of the local controlsystem 42. That is, the control/monitoring device 54 may be part of acollection of modules, such as a control system 100 (e.g., local controlsystem 42) depicted in FIG. 4.

As shown in FIG. 4, the control system 100 may include thecontrol/monitoring device 54 as a single module of a number of modulesthat perform various types of operations. For instance, the controlsystem 100 may also include an artificial intelligence (AI) module 102,an input/output module 104, and the like. The control system 100 may becoupled to a data backplane 106 that may facilitate communicationbetween modules of the control system 100.

For example, FIG. 5 illustrates a block diagram of the control system100 that may include the control/monitoring device 54 the AI module 102and the I/O module 104 coupled to each other via the data backplane 106.The data backplane 106 is provided over which multiple automationcomponents may communicate. As will be appreciated by those skilled inthe art, such backplanes may allow for physical mounting of modulardevices, such as automation controllers, input/output devices, and soforth. In the illustration, the AI module 102 may include processingcircuitry, memory circuitry, communications circuitry, and so forth toperform various types of analytical operations. Data communication overthe backplane 106 may allow for raw, process, or other data to beaccessed by the AI module 102 via other modules of the control system100, the I/O module 102, or the like. Data may also be output from thecontrol system 100 via the data backplane 106. For instance, the I/Omodule 104 may output visualization data to the HMI 52, which maypresent the visualization data via an electronic display.

As mentioned above, data is collected or accessible to local controllersand/or computing modules, such as the local control system 42, that arelocally present at the portion of the industrial automation system 10may include the relevant information that may be useful for respondingto a request for information. Indeed, in some embodiments, the localcontrol system 42 may be able to provide an answer for the request basedon data that is locally available if they receive the request. With thisin mind, it may be useful to ensure that requests for particular typesof information is forwarded to the appropriate local control system 42that has access to the relevant information.

For example, FIG. 6 illustrates a communication network 110 that routesrequests for information or data through a routing system 112 (e.g.,router) to a local control system 42 that may include the AI module 102,the control/monitoring device 54, the I/O module 104 and the like. Insome embodiments, the routing system 112 may include the componentsdescribed above as part of the control/monitoring device 54 and maycommunicatively couple to a number of local control systems 42, thecloud-based computing system 68, the computing device 66, and othersuitable communication-enabled devices. The routing system 112 may alsohave access to one or more databases 114 that may include informationregarding various types of data and the storage locations of the data.That is, in some embodiments, data pertaining to different portions ofthe industrial automation system 10, particular industrial automationdevices 20, industrial automation equipment 56, and the like may bestored in various locations within the network 110 including thedatabase 114. The databases 114 may include a list of storage locationsfor each dataset or type of data, such that the routing system 112 mayroute various types of data to certain devices or control systems foranalysis.

By way of example, the routing system 112 may receive a request from auser via the computing device 66 or the like to determine a cause fordowntime or reduced production or operation of line 2 in the industrialautomation system 10. After receiving the request, the routing system112 may determine the local control system 42A of the industrialequipment related to line 2 indicated in the request. In someembodiments, the routing system 112 may determine that datasets relevantto the operation of line 2 may also be present in the databases 114 orother data sources that are not local to the local control system 42A.In any case, the datasets related to the operation of line 2 may beretrieved from various the data sources, the industrial automationequipment 56, the databases 114, and the like by the routing system 112.

The routing system 112 may then route the identified datasets along withthe request to the local control system 42A that may be best suited todetermine the answer to the request. Like the control system 100 of FIG.5, the local control system 42A may include a collection of modules thatperform various tasks for the operation of the industrial equipmentrelated to line 2. That is, the local control system 42A may be a localcontrol system for controlling and monitoring the operations of line 2.One of the modules of the local control system may include the AI module102, which may have access to the data available to thecontrol/monitoring device 54 related to the operations of line 2. The AImodule 102 may be designed to perform certain types of analysis toidentify solutions and/or respond to requests provided to it. Using thedata available to the AI module 102, the AI module 102 may determine asolution or answer to the request and send the solution back to the uservia the network 110.

To determine the solution or answer to the request, the AI module 102may include certain analytic and/or machine learning algorithms thatenable the AI module 102 to parse the request and identify likelyanswers to the request based on various types of models, artificialintelligence methodologies, and the like. As such, the user of thecontrol system 100 may have access to real time solutions for potentialproblems or information requests without waiting for input fromindividual subject matter experts.

With the foregoing in mind, FIG. 7 illustrates a flow chart of a method120 for controlling the operations of the industrial automationequipment 56 or one or more industrial automation devices 20 using thecontrol system 100 that is locally connected to the datasets regardingthe industrial automation system 100. Although the method 120 isdescribed as being performed by the routing system 112, it should benoted that any suitable computing device or edge-computing devicecapable of communicating with other components in the industrialautomation system 10. The edge-computing device may include any suitablecomputing device 66 or control system 100 where data may be created,analyzed, or accessed, such that inferences, predictions, or the likemay be determined.

Additionally, although the method 120 is described as being performed ina particular order, it should be understood that the method 120 may beperformed in any suitable order. Referring now to FIG. 7, at block 122,the routing system 112 may receive a request for information regardingone or more particular industrial automation devices 20 from arequesting component. The requesting component may include the computingdevice 66 being operated by a user, a control system 100 that requeststhat information to perform a respective analysis, or the like.

In one embodiment, the request may include details related to theinformation requested. For example, the details may be related to aspecific portion of the industrial automation system 10, a conveyorsection of the industrial automation system 10, productivity associatedwith a portion of the industrial automation system 10, operatingparameters regarding the portion of the industrial automation system 10,or the like. By way of example, the request for information may berelated to operating parameters or outputs of the mixer 18, thedepositor 22, the oven 26, or other suitable machinery in the industrialautomation system 10 of FIG. 1.

At block 124, the routing system 112 may determine the likely locationof one or more datasets associated with the request. That is, therouting system 112 may query the database 114 or its internalmemory/storage to identify the storage locations for datasets associatedwith one or more parts of the request. For example, if the requestspecifies a particular industrial automation device 20, the routingsystem 112 may query a relevant index or the databases 114 to determinethe local control system 42 that controls or monitors the operation ofthe specified industrial automation device 20.

In addition to the location of the respective local control system 42,the routing system 112 may identify datasets that may be related to therequest. Continuing the example provided above, the routing system 112may determine that datasets associated with adjacent industrialautomation devices 20 or industrial automation equipment 56 may berelated to the specified industrial automation device 20. For instance,if the request was related to the depositor 22 of the industrialautomation system 10 in FIG. 1, the routing system 112 may determinethat datasets related to the operation of the mixer 18, the conveyor 24,or the oven 26 may be related to the depositor 22 since these componentsare directly connected to the depositor 22.

In one embodiment, the contextualization of data can be used to identifythe scope of the data that is needed to provide an answer to a requestfor information. More specifically, the available data may be structuredin a hierarchical manner where each variable is treated as a node withpotential parent node(s) and children node(s). For a request such as“what is the cause of down time on Line 2?,” a contextualized data mayenable intelligent filtering of the data that may be routed to the AImodule 102 for the analysis. The routing system 112 may also be equippedwith the intelligence to systematically interrogate the contextualizeddata to determine where a link in the data network can be eliminatedwithout preventing the AI module 102 from accessing the relevant data.As such, the intelligence or programming of the routing system 112 mayinclude a correlation analysis of the data, or a causality analysis ofthe data.

In some embodiments, at block 126, the routing system 112 may determinewhich local control system 42 may be best suited to determine the answerto the request. That is, the local control system 42 that has access tomost of the information or datasets related to the request may be bestsuited to perform respective analysis. Each local control system 42 mayinclude the AI module 102 mentioned above. The AI module 102 may performthe analysis to determine the answer or solution for the request forinformation. The request for information may include a question or querythat is provided in natural language. The AI module 102 may parse thenatural language request to determine an answer or response to therequest using model-based analysis as provided in U.S. patentapplication Ser. No. 15/720,705 or U.S. Pat. No. 10,073,421, both ofwhich is incorporated herein by reference.

At block 128, the routing system 112 may retrieve datasets identified asbeing relevant to the request at block 124. The routing system 112 maythen, at block 130, route the datasets from the respective storagelocations to the local control system 42 identified at block 126. Itshould be noted that, in some embodiments, the routing system 112 maynot route any datasets to the identified local control system 42. Thelocal control system 42 may have access to sufficient information thatmay enable the respective artificial intelligence module 102 to performthe respective analysis.

After the respective local control system 42 or the AI module 102performs the respective analysis, it may generate data that correspondsto a response or answer to the request provided at block 122. As such,the local control system 42 may provide the output data to the routingsystem 112, and, at block 132, the routing system 112 may receive theoutput data.

At block 134, the routing system 112 may route the output data back tothe requesting component of block 122. The requesting component maypresent the requested information via the display 86 of thecorresponding device, output the requested information via audio, or thelike. In some embodiments, the requested information may include acommand or instructions that may be related to resolving an issuerepresented in the request for information. Continuing with the exampleprovided above, the routing system 112 may receive a command from the AImodule 102 that the reason for the down time in line 2 may be related toan error message received via the control/monitoring device 54 regardingthe input voltage received by the industrial automation devices 20 ofthe depositor 22. As such, the routing system 112 may, at block 136,send a command to the local control system 42 for the depositor 22 toadjust the operations of the respective industrial automation devices20, such that maintenance personnel may troubleshoot or performmaintenance operations on the respective industrial automation devices20.

The command may include any suitable control or annunciation operation.For example, the command may include causing the depositor 22 to shutdown, decrease its operating speed, display an alarm or notification viathe display 86 of the corresponding control/monitoring device 54, or thelike. In some embodiments, the command may include instructions directedat multiple industrial automation equipment 56 that may be operated by anumber of control systems 100. As such, the routing system 112 maycoordinate the operations of the industrial automation system 10 basedon the analysis performed by the AI module 102 of one local controlsystem 42.

In some embodiments, the AI module 102 may send the command to thecontrol/monitoring device 54 of the local control system 42. In thisway, the local control system 42 may perform analysis regarding theoperations of the industrial automation system or a portion thereof andimplement the control operation adjustments without interacting withother computing devices 66 or the cloud-based computing system 68. As aresult, the integrity of the data communicated within the local controlsystem 42 may be increased because it is not communicated outside of thelocal control system 42. Moreover, by using the data available to thelocal control system 42, the AI module 102 may perform analysis moreefficiently to determine operation adjustments, thereby providingreal-time adjustments during the operation of the industrial automationapplication 64.

With the foregoing in mind, to improve the data integrity communicatedbetween components of the local control system 42, the routing system112, the computing device 66, and other communication-enable components,the AI module 102 or other module of the local control system 42 mayencrypt the data being transmitted from a respective module to preventothers from snooping, tampering, or modifying the communicated data. Byway of example, the data transmitted as output by the control system mayinclude an injected signature signal (e.g., controller certificate) thatmay cause the data communicated via the control system to becomeincomprehensible (e.g., obscure) by other devices. In certain cases, thesignature signal may distort the data, such that the data may not beinterpreted or analyzed for patterns or other insightful informationwith regard to how the data controls operations of an industrial device.

By way of example, the AI module 102 or other suitable module of thecontrol system 100 may use a model-based security validation process.That is, the AI module 102 may include information about asecurity-validation model explicitly deployed in the control/monitoringdevice 54 (e.g., controller) to establish the authenticity of the AImodule 102 after it is communicatively coupled to the control/monitoringdevice 54 via the data backplane 106. The AI module 102 may produce arandom signal as input to security-validation model, which may be storedin a local memory of the AI module 102. The AI module may create acorresponding output of the random signal applied to thesecurity-validation model. The AI module 102 may then send both theinput random signal and the output of the security-validation model tothe control/monitoring device 54. The control/monitoring device 54 maythen apply the input it receives from the AI module 102 to its localversion of the security-validation model. The control/monitoring device54 may then compare the output from its local model to the output signalit received from the AI module 102. If they match, the AI module 102 maybe validated and may be allowed to further communicate with thecontrol/monitoring device 54. If, however, the two outputs do not match,access between the AI module 102 and the control/monitoring device 54will be denied.

In some embodiments, the validation process described above may bedesigned and deployed in a manner that they do not interfere with thecontrol signals that the control/monitoring device 54 generates tocontrol the operation of the industrial automation equipment 56. Assuch, the validation process may not inhibit or slow down the speed inwhich the industrial automation equipment 56 receives operationaladjustments.

With the foregoing in mind, FIG. 8 illustrates a flow chart of a method140 for encrypting data output by the local control system 42. Althoughthe following discussion of the method 140 is described as beingperformed by the AI module 102, it should be understood that anysuitable module of the control system 100 may perform the process of themethod 140. In addition, it should be noted that the method 140 shouldnot be limited to the order presented; instead, it should be understoodthat the method 140 may be performed in any suitable order.

Referring now to FIG. 8, at block 142, the AI module 102 may receive therequest for information described above in FIG. 7. In addition, the AImodule 102 may also receive data retrieved by the routing system 112, asdescribed above with reference to block 128 of FIG. 7.

At block 144, the AI module 102 may determine the requested informationusing one or more algorithms stored therein, machine learningoperations, trend analysis, or the like. After determining the requestedinformation, the AI module 102 may, at block 146, encrypt the requestedinformation using a suitable encryption protocol. By way of example,FIG. 9 describes an example encryption process that may be employed bythe AI module 102. In some embodiments, the AI module 102 may encryptdata that may be output by the AI module 102 to other modules of thelocal control system 42. That is, if the requested information includesone or more commands that the AI module 102 may be able to initiate withthe respective industrial automation equipment 56, the AI module 102 maynot encrypt the output commands to ensure that the commands aretransmitted to the respective industrial automation equipment 56 in atimely manner.

Data communicated between modules or outside the local control system42, however, may be encrypted to protect the data from potential hackingattempts. As such, at block 148, the AI module 102 may output theencrypted information to a destination module or communication-enableddevice (e.g., computing device 66, cloud-based computing system 68).

Based on the requested information, at block 150, the AI module 102 maygenerate control signals or commands to adjust one or more operations ofthe industrial automation equipment 56. In some embodiments, the controlsignals generated by the AI module 102 may be directed to components orindustrial automation equipment 56 that is not locally connected to thelocal control system 42. As such, the encrypted information output tothe routing system 112 may be parsed or decrypted to identify thedestination of the control signals and the encrypted information maythen be routed to the desired control system 100.

Referring now to FIG. 9, to encrypt the requested information, the AImodule 102 or other suitable module may perform the method 160 describedherein. That is, in certain embodiments, at block 162, the AI module 102may retrieve a local security-validation model stored on a local memoryor storage accessible to the AI module 102. The security-validationmodel may be a model or algorithm that receives an input signal andproduces another signal that may be used as an encryption signal fordata communicated by the AI module 102.

At block 164, the AI module 102 may produce a random signal using arandom signal generator or other suitable device. The random signal maybe a waveform or a collection of values over time. In some embodiments,the random signal may be suitable to interface or be applied to datacommunicated by the AI module 102. For example, the random signal may bea string of ones and zeros. The string may be incorporated or applied toa model, such that the model uses the random signal to output adifferent signal.

At block 166, the AI module 102 may apply the random signal to the localsecurity-validation model retrieved at block 162. As such, the localsecurity-validation model may output a signal that distorts or changesthe random signal according to a respective algorithm or processassociated with the local security-validation model.

The output signal from the local security-validation model may be anencryption signal used to encrypt data communicated from the AI module102. At block 168, the AI module 102 may inject or incorporate theencryption signal into the requested information determined at block 144of FIG. 8. The AI module 102 may inject the encryption signal into therequested information in a number of ways. For instance, the AI module102 may use an embedding component or algorithm that may include one bitof the encryption signal in between each bit of the requestedinformation. In any case, the AI module 102 may incorporate theencryption signal into the requested information to generated encryptedinformation that may be transmitted to another component at block 170.In addition, at block 170, the AI module 102 may also output the randomsignal generated by the AI module 102 at block 164 to enable a receivingdevice to decrypt the encrypted information.

Keeping this in mind, FIG. 10 illustrates a method 180 for decryptingthe encrypted information determined via the method 160 of FIG. 9. Atblock 182, the receiving component, such as the control/monitoringdevice 54, the routing system 112, or other suitable receiving device,may receive the encrypted information along with the random signalgenerated at block 164.

At block 184, the control/monitoring device 54, for example, may applythe random signal to its locally stored security-validation model. Thatis, the control/monitoring device 54 may have access to asecurity-validation model that is the same as the security-validationmodel stored locally on the AI module 102. In certain embodiments,during initialization of the control system 100, one of the installedmodules may transmit the security-validation model to each of the othermodules communicatively coupled to the data backplane 106.Alternatively, during manufacturing, each module of the local controlsystem 42 may be programmed to include the same security-validationmodel to perform the tasks described herein.

By applying the received random signal to the locally storedsecurity-validation model, the control/monitoring device 54 maydetermine an encryption signal that is the same as the encryption signaldetermined at block 166 of FIG. 9. At block 186, the control/monitoringdevice 54 may use the encryption signal as a decryption signal or key todecrypt the encrypted information received at block 182. For instance,the control/monitoring device 54 may be aware of the injection protocol,process, or algorithm that the AI module 102 used at block 168 and mayperform an alternate process or scheme to determine the requestedinformation based on the encryption signal.

In some embodiments, the control/monitoring device 54 may receive justthe encrypted information from the AI module 102. In this case, usingjust the locally stored security-validation model and the encryptedinformation, the control/monitoring device 54 may determine theencrypted signal applied to the requested information and thecorresponding requested information. That is, the security-validationmodel may be backwards compatible to receive the encrypted informationas an input and may output the requested information and the encryptionsignal.

With the foregoing in mind, in some embodiments, each component ormodule of the local control system 42 may include a signature signalembedded in the payload of the requested information, at a headerportion of the requested information, or the like. That is, whenreceiving data, different components or modules of the control system100 or various receiving devices of the industrial automation system 10may check to determine whether an expected signature signal is presentin the communicated data before attempting to read or decrypt the data.In some embodiments, the signature signal may be extracted from a knownportion of the received data and applied to the security-validationmodel or other suitable model. The model may receive the signaturesignal and may produce an output indicative of whether the received datais to be trusted. As such, a number of signature signals may cause themodel to output a valid status. With this in mind, a number of signaturesignals may be locally stored with respect to the AI module 102, thecontrol/monitoring device 54, or the like and may be embedded or addedto the data being communicated.

Regardless of the security protocol undertaken between communicatingdevices or modules, the encryption of data in the local network (e.g.,computing device, edge device, control system) may enable the data to betransmitted and stored to a cloud-computing system (e.g., Factory TalkCloud), while limiting the vulnerability of the stored data from beingintercepted or compromised by unwanted users. Moreover, the presentembodiments presented herein may enable locally connected modules tocommunicate between each other while reducing the risk of the datacommunications being intercepted or compromised. In addition, the localanalysis of the request for information may enable the industrialautomation equipment 56 to adjust its operation more efficiently withoutrelying on data being transmitted outside of the local control system42.

Automated Locally Modeling of a Target Variable

With the foregoing in mind, the AI module 102 or other suitable programsmay be used to assist a user in identifying efficient operatingparameters for the industrial automation equipment 56 in the industrialautomation system 10. In certain embodiments, the AI module 102 mayreceive user input (e.g., subject matter expert) to assist learningalgorithms employed by AI module 102 to identify solutions to problems(e.g., requests for information) that may be defined by another user.For example, U.S. patent application Ser. No. 15,720,582, which isincorporated herein by reference, may use candidate variable inputs,which have been filtered by human operators, to identify decisionvariables to assist a user in operating various components of theindustrial automation system. That is, the candidate variable inputs maycharacterize certain input data as possible influencers to the problemstatement or request for information.

To effectively perform a driverless (e.g., unsupervised, without thepresence of a human in the workflow of the learning) learning algorithm,the AI module 102 may analyze data that may be available to AI module102 via the local control system 42 to identify a subset of the datathat may be causally related to the operation of a particular automationdevice 20, industrial automation application 64, or output of theindustrial automation system 10 without receiving human input. With thisin mind, in some embodiments, the AI module 102 may perform certainmethods to assist a user to control industrial automation devices 20 ofthe industrial automation system 10 without the involvement of otherhuman operators (e.g., subject matter experts) that may traditionally berelied on to evaluate the performance or integrity of the industrialautomation system 10.

As such, in certain embodiments, the AI module 102 may receive auser-defined target variable that corresponds to a variable or aspect ofthe industrial automation system 10 that the user is attempting tocontrol. For example, the target variable may include a prediction of avolume output of the industrial automation application 64. The AI module102 may then receive a number of data tags that may correspond todifferent types of data accessible to the AI module 102, which may becommunicatively coupled to the local control system 42 that locallycontrols the operation of a portion or the entire industrial automationsystem 10.

The data tags are received and interpreted by the AI module withoutjudgment or weight, such that the AI module 102 performs an agnosticanalysis of the received data. Based on the received data, data tags,and the target variable, the AI module 102 may begin identifying asubset of the received data as input variables that affect the targetoutput. The subset of the received data may then be used to model thetarget variable, thereby efficiently using the data that is more likelyto be causally connected to the target variable. As such, unlike neuralnetworks that continuously receive data from all available data sources,the AI module 102 systematically selects (e.g., through a mixed integernonlinear optimization) a limited subset of data that is available viathe local control system 42 to characterize or model the performance oftarget variable with respect to the limited set of data.

In one embodiment, the systematic selection process may include anexplicit mixed integer optimization that penalizes the number ofselected input variables to encourage parsimony of the model. Parsimonymay be a feature of the automated local modeling approach disclosedherein, as it facilitates the interpretability of the model by plantoperation.

Keeping the foregoing in mind, to perform the automated modelingoperations described herein, the AI module 102 may employ a systematicmethodology to determine whether a minimal number of available variables(e.g., data points, datasets) sufficient to build a model are available.It is well known by those skilled in the art of modeling that the moreinput variables are selected the smaller the prediction error couldbecome. However, to enable the AI module 102 to perform the modelingoperations described herein on data available to the local controlsystem 42 in real time or near real time (e.g., within second ofacquiring or receiving) in a manner that the resulting models aresuitable for use in real-time (e.g., to pin point or identify the causeof an impending anomaly), the AI module 102 may not be able to rely onlarge number (e.g., more than a threshold) of inputs. That is, as thelocal control system 42 receives streaming data (e.g., as data isreceived), the model created by AI module 102 is expected to be robustto noise and disturbance inherent in real time data, and a large numberof inputs for the model may adversely impact the ability of the AImodule 102 to stay robust. Accordingly, the AI module 102 may seek abalance between the model accuracy, the generalization capability, andthe robustness of the model that is built.

In addition, to build a model based on the streaming data, the AI module102 may be able to modify the selection of input variables when theoperation condition changes significantly (e.g., more than a thresholdpercentage). The change in the input subset selection, however, may beself-driven for the automated modeling paradigm to be viable. Aself-driven (e.g., automated) modeling therefore may contain the abilityto distinguish between the circumstances that (a) a simplere-parameterization of the selected model would be sufficient (in whichcase the variable selection and the functional form of the model remainsunchanged and constant coefficients in the model are retrained), (b)when variable selection is still valid but the functional form is nolonger suitable (e.g., when output becomes inversely proportional to aninput instead of being linearly proportional to that same variable undera previous operating condition), and (c) when a new set of inputvariables is warranted for selection. In each case, the self-driven AImodeling engine of the AI module 102 may carefully balance theinterpretability of the models for the operators/domain experts againstmodel accuracy and sensitivity to noise/disturbance variables. Theself-driven gradual escalation of the changes to the model therebyenables the real-time usability of the models, and is therefore a keydistinction of the self-driven (e.g., automated) modeling engine fromthe state of the art. As such, the AI module 102 may account for anothertrade-off in the modeling, where the change in variable selection istraded off against model quality, model robustness, and the number ofthe variables selected by the AI module 102 for automated modeling.

In certain embodiments, the AI module 102 may create new data thatdescribes a relationship between some dataset and the target variable.The new data may be available for view via the display 86, provided tothe control/monitoring device 54 to implement a particular controlscheme or strategy, used to indicate a set point for thecontrol/monitoring device 54, used to implement quality controlobjectives, and the like. In any case, the AI module 102 may be employedto identify certain variables or data points that may be used to controla target variable, identify certain variables or data points thatinfluence a target variable, and the like. By using the certainvariables or data points to characterize or model the target variable,the AI module 102 may not rely on historical data sampled more than athreshold (e.g., month) of amount of time ago to perform its modelingoperations. Instead, the AI module 102 may use recently sampled data(e.g., in the extreme case the streaming data) to create and verify itsmodel. For example, if a sensor input is sampled every 30 minutes, theAI module 102 may use a collection of the sampled data to perform itsmodeling operations and use the most recently sampled data to verify itsmodel.

Keeping the foregoing in mind, FIG. 11 illustrates a flow chart of amethod for identifying target variables for the AI module 102 to model.As mentioned above, the AI module 102 may identify the target variablesto model with limited or no human input to facilitate the automaticidentification of target variables to model. The AI module 102 maygenerally identify target variables to model based on identifying thevariables or data points that affect the operational state of theindustrial automation device 20, the industrial automation equipment 56,the industrial automation system 10, or the like.

Although the following description of the method 200 is described asbeing performed by the AI module 102, it should be noted that anysuitably programmed device with machine-learning code and algorithmsstored therein may perform the embodiments described herein. Moreover,it should be noted that the method 200 may be performed in any suitableorder and should not be limited to the order presented.

Referring now to FIG. 11, at block 202, the AI module 102 may receive anindication (e.g., desired target variable) of the industrial automationdevice 20, the industrial automation equipment 56, or portion of theindustrial automation system 10 to model. The AI module 102 may beassociated with the indication received at block 202 and may have accessto data via the data backplane 106, adjacent modules of the respectivelocal control system 42, or the like. The AI module 102 may rely on thedata locally available to it or via the data backplane 106 toefficiently perform the embodiments described herein.

At block 204, the AI module 102 may receive data and corresponding datatags from various data sources accessible to the AI module 102 via thedata backplane 106. As such, the AI module 102 may receive data fromadjacent modules, local storage components, industrial automationequipment 56 communicatively coupled to a module of the local controlsystem 42, and the like. The data tags may characterize a type of datathat corresponds to a respective piece of data. However, it should benoted that the AI module 102 may merely receive the data tags and maynot place any weight or judgment on the respective data based on thecorresponding data tags. In this way, the AI module 102 may analyze thereceived data in an unbiased manner without including preconceived orassumed relationships between two datasets. For example, the AI module102 may receive voltage data and temperature data, but for analysispurposes, the AI module 102 may just receive raw values for the voltagedata and the temperature data and disregard the fact that the raw datacorresponds to voltage and temperature. In this way, the AI module 102may not attempt to characterize the voltage data as being related to thetemperature data unless the AI module 102 identifies a relationshipbetween these two datasets without regard to their context.

At block 206, the AI module 102 may perform a non-contextual or unbiasedclustering of the received data. That is, the AI module 102 may applyefficient clustering algorithms (e.g., for stream clustering) to theunlabeled data (e.g., data without data tag context) to create insightinto operational data of the industrial automation device 20, theindustrial automation equipment 56, or the portion of the industrialautomation system 10 specified at block 202.

At block 208, the AI module 102 may receive data available to thecorresponding local control system 42 to determine changes in theoperation state of the respective industrial automation device 20, therespective industrial automation equipment 56, or the respective portionof the industrial automation system 10 that are associated with distinctclusters of the unlabeled data in the data (e.g., real-time dataacquired via sensors 16, generated by modules, etc.) received by the AImodule 102. For example, FIG. 12 illustrates a multi-dimensional graph220 that has five identified clusters based on streaming data receivedby the AI module 102. As mentioned above, the streaming data maycorrespond to data representative of real time or near real time data(e.g., acquired or generated within seconds).

Referring briefly to FIG. 12, different data points may be plotted onthe multi-dimensional graph 220 via mapping to an information space thatmay not directly correspond to any of the measured data (e.g., a spaceformed by most principal or highly influential components of themeasured data). As such, each dimension of the multi-dimensional graph220 does not directly map to a physical measurement. Instead, each datapoint received by the AI module 102 is mapped into a new informationspace, where the answer to the requested information may be providedmost clearly. In certain embodiments, the multi-dimensional graph 220may be presented via the display 86, such that operation personnel mayprovide input with regard to labels for the identified clusters. The AImodule 102, in some embodiments, may store the input label (along withany action deemed appropriate by the personnel) as an evolving “expertsystem” that captures operator's/domain-expert's knowhow in a systematicmanner.

With this in mind, in some embodiments, when a new data point isreceived that does not fit in a previously identified cluster, the AImodule 102 may produce an alarm for the operator to evaluate the newdata point. The operator may then provide context or information relatedto the new data point to assist the AI module 102 to continue toclassify received data points. It should be noted that the clustering ofdata points performed by the AI module 102 is performed without anycontext related to the received data. As such, the output of theclustering step could act as an input to the automated modeling engineto convey the change in the operation state of the control system andhence allow a more accurate modeling of the process where the rightmodel will be built for each of the substantially different operatingregimes of the process (control/automation system) without human input.As a result, the AI module 102 may improve its ability to determine andanalyze operational parameters of the industrial automation system underdiverse operating conditions that the industrial automation system 10 orportions there of experiences without the assistance of subject matterexperts. The input from subject matter experts when available, however,can be incorporated into the clustering and modeling exercise (e.g., asconstraints in a constrained optimization used for clustering ormodeling).

Referring back to block 208, the AI module 102 may associate operationalstate data for the respective industrial automation device 20, therespective industrial automation equipment 56, or the respective portionof the industrial automation system 10 with each distinct clusteridentified at block 206. The operational state data may be received atblock 204 or may be associated with a time in which the correspondingdata was acquired. For instance, the AI module 102 may compare the datapoints of cluster 222 in the multi-dimensional graph 220 to thecorresponding operational state data for the respective industrialautomation device 20, the respective industrial automation equipment 56,or the respective portion of the industrial automation system 10. If athreshold number of the data points in the cluster 222 correspond to therespective operational device operating at a temperature above athreshold, the AI module 102 may classify the cluster 222 as a hightemperature operation cluster.

At block 210, the AI module 102 determine one or more models of theoperational states of the respective industrial automation device 20,the respective industrial automation equipment 56, or the respectiveportion of the industrial automation system 10 based on relationshipsbetween the clusters and the operational states. That is, based on theself-identified clusters, the AI module 102 may train itself to model orcharacterize the operational parameters of the respective industrialautomation device 20, the respective industrial automation equipment 56,or the respective portion of the industrial automation system 10. Forinstance, the AI module 102 may receive a new data point that may notfit into an existing cluster. If the data point is a first occurrence,the AI module 102 may ignore the data point as an outlier. However, if asimilar data point occurs again at a later time, the AI module 102 maybegin a process to create a new cluster and associate the new clusterwith a new operating parameter or condition. The relationship betweenthe data points in the cluster and the associated operational state maybe used to determine respective model for the operational state thatvaries according to the data points. For example, if a certain subset ofavailable process/automation system variables are found to determine thecreation of a specific cluster, the AI module 102 may now retrieve thecorresponding data tags of the data points to provide discernable orclear variables that a user may observe to determine the behavior of themodel.

At block 212, the AI module 102 may identify one or more data points(e.g., target variables) that may affect the operation of the respectiveindustrial automation device 20, the respective industrial automationequipment 56, or the respective portion of the industrial automationsystem 10 more than other data points. These data points or the subsetof data points in a particular cluster or a group of clusters may bedetermined based on the unbiased analysis of the data points and therelationships between the data points within the respective cluster. Forexample, the outcome of an AI engine for clustering can be used toselect certain data points that determine significant (e.g., more than athreshold) changes in the system's operating condition, and, as such,designate them as target variables for an automated causality modelingby the AI module 102 for automated modeling. As used herein, the AIengine may include one or more software applications or hardwarecomponents that perform the embodiments described herein within the AImodule 102 or other suitable computing device.

In some embodiments, the AI module 102 may solve an optimization problemwith regard to pruning the data points available to the AI module 102 toidentify a smaller (and manageable) subset of data points that influencedata points and designate these data points as target variables. Forexample, FIG. 13 includes a flow chart of a method 230 for identifyingthe target variables of a particular cluster of data points.

Referring to FIG. 13, at block 232, the AI module 102 may receivemultiple data points as potential target variables. The multiple datapoints may correspond to each data point of an identified cluster, asdescribed above. At block 234, the AI module 102 may model the behaviorof each target variable based on an unbiased analysis of each data pointand the relationship between each data point in the cluster.

Based on the analysis of block 234, the AI module 102 may generate adirected graph where each data point is represented as a node at block263. By way of example, FIG. 14 illustrates an example directed graph250 that includes five data points (x₁-x₅). The unbiased analysis ofblock 234 may determine whether a relationship exists between one ormore of the data points. Based on the identified relationship, the AImodule 102 may determine one or more directed links between the nodes.The directed links may be represented by the arrows that point from anoriginating node to a destination node. The originating nodes maycorrespond to the target variables that were modeled by the AI module102 at block 232.

Since the AI module 102 models a number of data points or targetvariables in parallel, a number of loops may be generated in thedirected graph 250. For example, data point x₁ includes a directed link252 to data point x₃, data point x₃ includes a directed link 254 to datapoint x₃, and data point x₂ includes a directed link 256 back to datapoint x₁. Since each of these data points relate to each other in aloop, there may not be a significant relationship between these datapoints.

With this in mind, at block 238, the AI module 102 may prune thedirected graph 250. In one embodiment, the AI module 102 may solve aproperly formulated optimization problem to break the existing loops inthe web generated by the directed graph 250. A potential objectivefunction is to minimize the increase in overall prediction accuracy andthe number of the links being cut in order to eliminate the loops in theweb.

In one embodiment, the AI module 102 may employ a pruning algorithm thatfocus on eliminating links that are less likely to be indicative of avariable that affects the operation of the respective industrialautomation device 20, the respective industrial automation equipment 56,or the respective portion of the industrial automation system 10. Onepotential criterion in the pruning stage is to remove all the linksbetween two nodes where the link is the only connection between thenodes. For example, since data point x5 has just one directed link 258connected between it and data point x3, the AI module 102 may remove thedirected link 258. In addition, the AI module may remove directed linksin which the correlation between the behaviors of the data points at therespective nodes exceeds a threshold (e.g. 95%). The node(s) for whichthere is no links after this pruning stage can be removed from thenetwork as there is another variable that perfectly captures its impacton the rest of the network.

With the foregoing pruning operation described above, the AI module 102may prune the directed graph 250 of FIG. 14 to the pruned directed graph270 of FIG. 15. As shown in FIG. 15, after the directed links 252, 254,256, 258, and 260 were removed due to being part of a loop or being thesingle link between two nodes, the remaining nodes includes data pointsx₁, x₂, and x₄. At block 240, the AI module 102 may select these targetvariables to perform iterative modeling. As such, the AI module 102 maycontinue to perform modeling operations on the selected data pointsbased on newly received data to verify the accuracy of the models and tofine tune the model. The models of the selected target variables maythen be used to control the operations of the respective industrialautomation device 20, the respective industrial automation equipment 56,or the respective portion of the industrial automation system 10communicatively coupled to the AI module 102. In this way, the AI module102 may automatically choose variables to model to efficiently controlthe operations of the respective industrial automation device 20, therespective industrial automation equipment 56, or the respective portionof the industrial automation system 10. The presently disclosedembodiments is of particular value in large systems where the sheernumber of tags makes a domain expert's job to identify a variable ofsignificance as the target for the automated modeling engine extremelydifficult. The above-mentioned methodology will use the informationcontent of the measured data as the basis for the identification ofpotentially critical variables in the operation.

Following the pruning stage, the AI module 102 may highlight or presenta variable with larger number of incoming links as a potential variableof interest to the experts. In other words, the AI module 102 mayidentify the key convergence points of data as target variables for theautomated modeling engine. The modeling AI module 102 may then beadjusted to optimize or model these target variables as mentioned above.

Based on the processes described above, the AI module 102 may beemployed to analyze the quality of the performance of the industrialautomation system, determine a root cause for a particular problem oroutput of the industrial automation system, provide prediction outputsfor soft sensing capabilities, and the like in an entirely self-drivenmanner. In one embodiment, upon the occurrence of a noteworthy event(e.g., a stoppage in a production line, a breakdown of an equipment, ora failure of the product quality test), the discovery function of theself-driven AI engine of the AI module 102 may be activated first. Inthe course of this discovery process a number of nodes may be identifiedas candidate target variables with potential relevance to the observedevent. Each of the candidate nodes may then be sent to the self-modelingfunction of the AI engine in the AI module 102 where in the mannerdescribed earlier a minimal subset of the process variables may beselected to model the candidate nodes. The predicted value of thecandidate node may then be analyzed (e.g., via clustering function ofthe AI engine) to determine whether a change in predictability of thecandidate target variable could provide an advance notice for theimpending event. A notification may be sent to the domain expert via theAI module 102 and the feedback from the said expert may be used todesignate the candidate node as a target variable to be monitored intothe future. Example applications for the AI module 102 includeminimizing fuel consumption for various operations, predicting whenproduct quality will be less than a threshold for a particular batch,predict whether manufactured goods will pass stress tests, determineemission levels in real time, monitor pumps and other equipment foranomalies, and the like.

Visualizing Relationships Between Target Variables

As discussed above with respect to block 240 of FIG. 13, in certainembodiments, the AI module 102 may repeatedly analyze the receiveddatasets to glean additional information regarding the target variablesduring each iteration. For example, the early iterations of analysis mayinvolve self-learning (e.g., identifying relationships) between the datapoints (e.g., target variables), while the later iterations of analysismay involve identifying a statistical feature of the identifiedrelationship or strength of correlation between the related data points.In addition to the strength of correlation between data points, the AImodule 102 may identify a directionality for the relationship, asillustrated in the directed graph 250. In one embodiment, the AI module102 may be equipped with an auto-excitation functionality that (a)monitors the normal operation of the automation process to establish alevel of signal that is sufficient for the AI engine to interrogatedirectionality of impact but is not large enough to disrupt normaloperation of the industrial automation system 10. That is, as discussedabove, the AI module 102 may determine which data point may affect theperformance or output of another data point. By analyzing the datapoints provided to the AI module 102 in this manner, the AI module 102may provide better context for determining relationship informationbetween different operations of different components (e.g., devices,equipment, portions) of the industrial automation system.

In one embodiment, the strength of the relationship between two datapoints may be represented by adjusting a visual characteristic orproperty (e.g., thickness, pattern) of the line between the two points.In addition, the directionality of the relationship may also be depictedvia an arrow in the line as depicted in the directed links of thedirected graph 250 in FIG. 14. Such visual representation of theautomation process can be used for performance monitoring if thedirected graph 250 is updated in real-time based on current operationdata. For example, if a correlation or connection between two nodesfalls below a threshold (in the extreme case disappears), then the AImodule 102 may generate an alarm accordingly.

With the foregoing in mind, FIG. 16 illustrates another example directedgraph 280 that may be generated by the AI module 102 that depicts therelationship between data points using a line, a strength of therelationship using a thickness of a line, and a directionality of therelationship using an arrow disposed on the line. Although therelationship, strength, and directionality is visualized in FIG. 16using a line, a thickness, and an arrow, it should be noted that othervisual effects may be used to visualize the respective properties.

Referring to FIG. 16, directed link 282 between data points x₆ and x₇and directed link 284 between data points x₇ and x₉ may indicate thatthe same strength of correlation exists because the weight or thicknessof the directed links 284 and 286 are substantially similar. Thedirected link 286 between data points x₈ and x₇, however, is thickerthan the directed links 282 and 284, thereby indicating that thedirected link 286 has a stronger strength of correlation.

The arrows depicted on each directed link may be representative of adirectionality of influence or effect between the data points. That is,the arrow of directed link 286 pointing from data point x₈ to data pointx₇ indicates that the data point x₈ influences or causes a change todata point x₇.

It should be noted that since the AI module 102 does not apply weights,denote data tags, provide metadata, or the like to the data points, theAI module 102 agnostically (e.g., unbiased) analyzes the data pointsregardless of what the data points represent. In this way, the AI module102 is free from bias that may normally occur from human analyzers thatmay tend to influence the human analyzers' decision.

In some embodiments, after the AI module 102 generates the directedgraph, the AI module 102 may present the directed graph via the display86 or other suitable device. As such, an operator may view and interpretthe directed graph with the data points and the relationship informationpertaining thereto. In this way, the operator may adjust the operationsof certain components in the industrial automation system 10 based onthe relationships between the various data points—not based on histraditional understanding. It should be noted that the unit-lesspresentation of data points provides the operator with a nontraditionalperspective of the operation of the respective modeled data points. Assuch, the operator may be free from natural biases or previousexperiences affecting his judgment or analysis.

For example, in a water treatment plant, an operator may control theoperations of various processes involved in treating water received fromvarious sources. Operators may believe that because they control theprocesses for the water treatment, they are responsible or directlyattributable to the quality of the water treatment. However, the AImodule 102 may perform an analysis of the data points and determine thatwater originating from a particular location or a machine that is usedprior to the operations managed by the operator may affect the qualityof the water treatment more than other data points that are directlycontrollable by the operator. Since these operations may occur beforethe operator has any control over the water treatment process, theoperator may not determine or suspect that the actual problem causingthe water treatment quality to decrease is occurring prior to theoperator's involvement or control. After identifying the most impactfuldata point via the directed graph described above, the AI module 102 mayassign the identified data point as a target variable and generate amodel that aims to control the target value with respect to improvingthe water quality of the water treatment plant. In some embodiments, theAI module 102 may identify a number of data points that may affect thewater quality, and the AI module 102 may model these data points withrespect to the water quality in accordance with the embodimentsdescribed herein.

With the foregoing in mind, FIG. 17 illustrates a flow chart of a methodfor automatically identifying target variables to model and determiningstrength of relationships between target variables, in accordance withembodiments described herein. Like the other methods described above,the method 300 may be performed in any suitable order by any suitabledevice.

The following description of the method 300 includes the AI module 102performing a methodology for identifying or down selecting a group ofdata points that may affect another data point, determine thedirectionality of the effects, develop a model that characterizes theidentified data points with respect to a target variable, and present avisualization that represents the relationships between the identifieddata points and the target variable. In some embodiments, a first engineor AI module 102 may perform the down selection of data points, a secondengine or AI module 102 may determine the directionality, and a thirdengine or AI module 102 may perform the modeling. It should be notedthat unlike the 200 of FIG. 11, the present discussion of the method 300include one or more predefined target variables.

Referring now to FIG. 17, at block 302, the AI module 102 may receivedata points from locally accessible data sources as described above. Inaddition, the AI module 102 may receive one or more target variablesthat the AI module 102 may model or identify relationships between thereceived data points and the target variables.

At block 304, the AI module 102 may map the received data points to aninformational space, similar to the multi-dimensional graph 220 of FIG.12. In one embodiment, the AI module 102 may employ a PrincipalComponent Analysis (PCA) methodology to map the data points to theinformational space. The PCA methodology may involve extractinginformation content of the data points. For example, the AI module 102may employ the PCA techniques to determine directions of maximalvariation in the information content of the data points. As such, the AImodule 102 may reduce the dimensionality of data analysis by mapping thereceived data points into an information space, where redundantinformation or data points will project onto a single representativefeature.

At block 306, the AI module 102 may reduce redundancies in the mappeddata points. That is, in one embodiment, the AI module 102 may employ arecursive PCA operation to eliminate the redundancy in the raw datapoints and use PCA components as candidate input variables for themodeling the target variables received at block 302. The PCA componentsmay be an output of the PCA process based on a set of observations ofpossibly correlated variables (e.g., data points) into a set of valuesof linearly uncorrelated variables called principal components.

By employing the recursive PCA process, the AI module 102 may increase aspeed in which parallel-modeling processes can be employed. For example,the AI module 102 may treat each data point (at the extreme case) as atarget variable for parallel modeling using one AI module, a number ofAI module 102, or other suitable devices.

Using the results of the recursive PCA process, at block 308, the AImodule 102 may identity relationships or links between the PCAcomponents, the data points, and the target variables. At block 310, theAI module 102 may perform an efficient pruning exercise where each ofthe target variables that share identical PCA components as input nodesare analyzed to determine whether the target variables are redundant.That is, the AI module 102 may determine that target variables that havesimilar input variables and similar functional dependencies are likelyredundant identical information content. In this case, the AI module 102may keep one target variable from each group. In addition, the AI modulemay identify one or more target variables with large number of links(e.g., more than a threshold). In one embodiment, the AI module 102 maycharacterize these target variables as target variable of interest,which may be reported to the operator for potential monitoring,automated monitoring by the control/monitoring device 54, assigned bythe AI module 102 as a target variable for automated modeling engine tobe modeled as a function of available original data points whereredundancy in the original variable space is removed, and the like.

After the relationships are pruned, the AI module 102 may, at block 312,generate a model of the target variables received at block 302 based onthe pruned relationships. At block 314, the AI module 102 may determinethe strength of the relationships between data points and the modeledtarget variables based on the generated model. That is, after the modelfor the target variables is built, the AI module 102 may employ manystrategies to determine the significance or strength of any link betweenan input node and the target variable that is modeled. By way ofexample, the AI module 102 may employ model-based metrics (e.g., Sum ofSquared Errors (SSE) or Percent Variance Explained), data-based metrics(e.g., Linear Correlation Coefficient, Distance Correlation Coefficient,and Maximal Information Coefficient), or the like. In one embodiment,the AI module 102 may systematically remove a source node from a givenmodel and observe the deterioration in prediction capability of themodel. The link whose removal results in the largest deterioration ofprediction capability may be designated by the AI module 102 as thestrongest link, and thus, in one embodiment, displayed with thickestwidth in the corresponding directed graph. In some embodiments, theabove-described process may be referred to as an automated influenceanalysis.

With the foregoing in mind, the AI module 102 may use the generatedmodel and the corresponding strengths of relationships to detectcausality or a root cause of a condition that is affecting theperformance of a target variable. Generally, the detection of causalityis a computationally expensive exercise. In many cases, the predictionquality is the sole objective of the modeling exercise, and, as such,the exact causal relationship is not of primary interest. However, inmodel-based root cause analysis applications, the notion of causality isof primary interest. In some embodiments, most techniques for “causalinference” in time series data fall into two broad categories: (1) thoserelated to transfer entropy; and (2) those related to Granger causality.Transfer entropy and Granger causality are known to be equivalent undercertain conditions. In some embodiments, the AI module 102 may alsoincorporate a casual inference technique, called ConvergentCross-Mapping (CCM), is to analyze causality for time series innon-separable dynamic systems. It should be noted that the CCM algorithmmay be computationally expensive for the AI module 102 to implement. Assuch, the AI module 102 may use the output of the influence analysis bythe self-driving modeling engine in the AI module 102 to reduce thecomputational complexity of the CCM algorithm via reducing the number ofthe paired candidate variables to be tested for causality. The filteringapplied by the influence analysis may thus be very useful when potentialnumber of variables is greater than some threshold.

In one embodiment, the AI module 102 may employ the CCM algorithm as anindependent process whenever an identified model (e.g., that includes anode as target variable, one or more nodes as input variables, linksindicating the connection between input and target variables, andfunctional form describing how target variable is modeled as a functionof input variables) is flagged by the operator or the AI module 102 for“causality analysis.” In this case, the AI module 102 may trace thecause or strongest contributing component or data point of a particularcondition to one or more data points.

Referring back to the method 300 of FIG. 17, as the AI module 102generates values for various target variables, it may also generate avisualization to present the different states the target variables willassume in an information space similar to the multi-dimensional graph220 of FIG. 12. For example, the target variable values for differentoperational scenarios may be mapped into an information space andplotted on a chart and may be classified by the AI module or a user as aparticular operating parameter or condition. Referring briefly to FIG.12, the multi-dimensional graph 220 includes a 3-dimensional informationspace where a potentially large number of measured target variables aremapped to clearly indicate various states a system assumes as it issimulated for various conditions or parameters.

By way of example, in the multi-dimensional graph 220, cluster 222 maybe classified as an operating parameter when wiring temperature is abovesome threshold, cluster 224 may be classified as excessive vibration,cluster 226 may be classified as excessive power consumption, and thelike. In some embodiments, the AI module 102 may plot the targetvariable values and determine likely classifications for each cluster orreceive a user input that classifies each cluster.

In some embodiments, the target variable values may be plotted ininformation space that may allow a user to visualize operating points ordifferent operating conditions of the industrial automation system 10.To plot the target variables in the information space, the AI module 102may align the raw data from various sources according to a sequence thatcorresponds to a time in which the data was acquired. The sequentialdata may then be provided to the AI module 102 without time stamps, suchthat the AI module 102 may analyze data sets together without knowledgeof their time stamps.

Keeping this in mind, FIG. 18 illustrates a method 320 for plotting datapoints in informational space. Like the other methods described above,the method 320 may be performed in any suitable order by any suitabledevice.

At block 322, the AI module 102 may receive target variable datasetsover time. At block 324, the AI module 102 may align the target variabledatasets according to a sequence that corresponds to the time in whichthe datasets were received. As mentioned above, to plot the targetvariable datasets in the information space, the AI module 102 may alignthe raw data from various sources according to a sequence thatcorresponds to a time in which the data was acquired. For instance, FIG.19 illustrates a sample graph of position, torque, and velocity valuesaligned with respect to sequence.

As shown in FIG. 19, certain collections of values may be characterizedwith respect to different ranges (e.g., Ranges 0-4). Each range maycorrespond to a particular condition or operational state of arespective industrial automation device 20, a respective industrialautomation equipment 56, or a respective portion of the industrialautomation system 10. That is, the AI module 102 may determine thatRange 0 values correspond to a condition when the wiring temperature isabove some threshold.

At block 326, the AI module 102 may group the target variable datasetsaccording to the detected ranges or sequences. At block 328, the AImodule 102 may analyze the grouped target variable datasets according toconditions or operational states of a respective industrial automationdevice 20, a respective industrial automation equipment 56, or arespective portion of the industrial automation system 10 that islocally accessible or associated with the AI module 102. In oneembodiment, the target variable values for each sample in each range maycorrespond to a different operating parameter of a correspondingmachine.

After analyzing the grouped target datasets, the AI module 102 may, atblock 330, plot the target variable values an information space withrespect to each range, as depicted in FIG. 20. In some embodiments, thevisualizations generated by the AI module 102 may enable the AI module102 or a user to better identify various situations or conditions thatmay be present in the industrial automation system. In addition, certainfeatures, such as a drop-down feature, may be added to the visualizationto allow a user to better interact with the visualization. For example,the drop-down feature may include an option to send raw data or relatedto regarding a selected data point or cluster to an expert. In anotherexample, the user interactive feature of the visualization may allow theuser to reclassify certain data points, designate certain data pointsthat are not currently classified as part of a particular cluster,narrow the data points used for a particular cluster, and the like. Inaddition, the AI module 102 may alter the depiction of certain clustersor data points to provide an indication to an operator regarding anupcoming operational parameter, condition, or the like.

As shown in FIGS. 12 and 20, the target variable datasets may be groupedin a three-dimensional space or two-dimensional space, respectively. Inone embodiment, the AI module 102 may provide the ability for a user toselect the information space to which the raw data is mapped based onthe user's goal/objective. For example, a two-dimensional view might bepreferred by operators, as opposed to a three-dimensional view.Referring back to FIG. 12, in some embodiments, the AI module 102 maymap the three-dimensional plots to the two-dimensional space (e.g., dim1and dim2 of FIG. 12). In this way, cluster 222 and cluster 228 maybecome indistinguishable. In contrast, while mapping to anothertwo-dimensional space (e.g., dim1 and dim3 of FIG. 12), the AI module102 may provide a maximal separation of the cluster 222 and the cluster228. By presenting the clustered data to a user in a format that may beuseful for the user to discern, the AI module 102 may enable operatorsto adjust operations in the respective industrial automation device 20,the respective industrial automation equipment 56, or the respectiveportion of the industrial automation system 10 more efficiently.

Additionally, by employing the methods 300 and 320 and other methodsdescribed above, the AI module 102 may send an alert or signal to thedisplay 86 to notify an operator or the control/monitoring device 54 toadjust operations to avoid problems, increase efficiency, or the like.For example, the signal may indicate that a product being produced bythe industrial automation system has a quality that is less than somethreshold since the current target datasets are mapped in theinformational space corresponding to the quality issue. In addition, theAI module 102 may provide a recommendation with regard to a change inthe operation of the industrial automation system to correct thedetected problem. For instance, the AI module 102 may recommend to use ahigher temperature for a certain portion of a process, stop thehigh-speed production of a product, or the like based on its analysis ofthe condition. Alternatively, the AI module 102 may work to identify theroot cause of the problem based on the data points.

Industrial Control Based on Modeled Target Variables

In certain embodiments, the AI module 102 may identify a target variableto model to address a concern or control another target variable or datapoint according to a specified condition. To model the target variable,the AI module 102 may use the available data to determine an expectedmeasurement for the target variable with respect to time and expectedconditions. In addition to modeling the target variable, a problemstatement or operational condition may be defined for the model targetvariable. For example, a user may specify that the target variableshould remain within a particular range of values. The AI module 102 maygenerate a visualization that represents various operating points of theindustrial automation system 10 or portions thereof, a particularindustrial automation device 20, or the like. In this way, each datapoint may be tagged, categorized, or clustered according to acorresponding operation point.

By way of example, referring back to the example control system 100 ofFIG. 5, the data backplane 106 of a local control system 42 may includea control/monitoring device 54, the I/O module 104, and the AI module102. In certain embodiments, the control system may receive a number ofdefinitions or tags for different variables that an operator may wish tocontrol. These variables (e.g., target variables) may or may not bedirectly related to a parameter or measurement that may be acquired by asensor or the like. In some cases, the target variable may not beassociated with a predictable behavior due to the complex interworkingand interdependencies between components of the industrial automationsystem. As such, the AI module 102 may model the target variable basedon the available data and simulated data.

In certain embodiments, the AI module 102 or other suitable component(e.g., control/monitoring device 54) may receive a number of definitionsor tags for different variables that an operator may wish to control.These variables (e.g., target variables) may or may not be directlyrelated to a parameter or measurement that may be available to the localcontrol system 42. In some cases, the target variable may not beassociated with a predictable behavior due to the complex interworkingand interdependencies between components of the industrial automationsystem 10. As such, the AI module 102 may model the target variablebased on the available data and simulated data.

To perform these operations, the AI module 102 may receive data pointsx₁-x₁₀₀ and characterize each data point x_(n) as a particular variabletype. For example, variable type 1 may indicate that the data point isthe target variable for modeling, variable type 0 may indicate that theAI module 102 should evaluate the data point as a potential variablethat affects the target variable, variable type −1 indicates that the AImodule 102 should not consider this data point (e.g., data point isunrelated to the target variable), and variable type −2 may indicatethat the data point is a categorical variable. The categorical variablemay provide an indication with regard to a category or productattributed to the data point. For example, if one data point isattributed to three different products produced in the industrialautomation system 10, the AI module 102 may use this insight todetermine how the data point affects different data points whenoperating to produce different products. By receiving the data pointsand systematically defining a variable type for each data point, the AImodule 102 may initially generate a random network of data points, andthen prune the network to focus on the data points that affect otherdata points as discussed above.

In certain instances, the AI module 102 may initially ignore data pointsthat affect a number of other variables. For example, when modeling aperformance of a chiller, the AI module 102 may receive data pointsrelated to the airflow, temperature, change in temperature, pressure,and humidity. Humidity, however, may affect each of the other datapoints, such that the AI module 102 may not be able to gain any insightinto the interworking or interdependencies between the data points dueto the strong correlation between humidity and the other data points. Assuch, the AI module 102 may initially ignore the data points associatedwith the humidity and determine the interdependencies without thehumidity data. However, when determining the model of the chiller, theAI module 102 may then reintroduce the humidity data.

It should be noted that the AI module 102 is able to produce robustmodels of the industrial automation system 10 in a self-driven manner bytaking advantage of non-random relationship governing the operation ofthe industrial automation system 10 or a portion thereof. In oneembodiment, the AI module 102 may target one or more data points formodeling (e.g., the target variables can be determined by a humanoperator/plant manager/data scientist and alike), and identify a subsetof available process data as inputs for a model for the targetvariable(s). The deterministic nature of the physical industrialautomation system may enable the AI module 102 to capture a functionaldependency between input process variables and the one or more targetsthrough self-learning that may remain valid even when the inputs to thephysical process are noisy or subject to unknown disturbances. As such,the AI module 102 may analyze the data points with regard to the datapoints operating in a predictable nature with respect to the operationof the industrial automation system 10 or portions thereof. Thepredictable nature may thus be characterized by a variety of functionsthat are auto-discovered by the AI module 102 (e.g., through asystematic mixed integer nonlinear optimization) that corresponds to alogical effectual relationship between data points.

With the foregoing in mind, an example for controlling a motor (e.g.,industrial automation device 20 or equipment 56) is provided blow. Inone embodiment, a motor may be coupled to the I/O module 104 of the databackplane 106. The I/O module 104 may receive data (e.g., torque,position, velocity data) from sensors 16 disposed on the motor and makethe data available to the AI module 102. If a user requests that aparticular target variable x₁ is monitored, the AI module 102 may begincollecting available data via the I/O module 104 and other data sourcesand begin to plot the target variable in a chart (e.g., 2-dimensional or3-dimensional) with respect to different operating parameters fordifferent components of the industrial automation system 10 or portionsthereof. In some embodiments, the AI module 102 may label the modeledvariable (e.g., Aware_X₁) for the target variable x₁ according to theparameters of FIG. 21 with a pre-defined naming protocol that willautomatically prompt another AI module (or a different executable on thesame AI Module 102) to action. For example, an AI module responsible forthe tuning of a proportional-integral-derivative (PID) controller in theindustrial automation system 10 may periodically scan the data backplane106 and deploy a text-processing functionality to decode the variablename Aware_X₁ as the soft-sensor value to be used by the control system100 or the local control system 42.

The incorporation of a common vocabulary for the AI engines deployed atthe industrial automation layer may enable self-configuration andself-maintenance in a large application throughout the entiremanufacturing enterprise. In one embodiment, this common vocabulary maybe shared by a machine learning solution that is deployed at a serverlevel or in the cloud-based computing system 68. In particular, therouting system 112 in FIG. 6 could serve as the coordinator of thecommunication between industrial automation system 10 and thecloud-based computing system 68 allowing independence (and simplifyingcommunication challenges) for various AI engines deployed throughout theenterprise (e.g., communication network 110).

As shown in FIG. 21, the parameters may include a simulated value forthe target variable

, a minimum value, a maximum value, and a variable type to categorizethe target variable

. It should be noted that the parameters may include additionalcomponents (e.g., average value) or fewer components as depicted in FIG.21. The AI module 102 may use the data type to identify different datapoints accessible to the AI module 102 to model the target variable

. It should be noted that the parameters may include a problem statementthat the AI module 102 is designated to solve for the target variable

. The problem statement may also be defined as a function or the like.For example, the function may include minimizing a difference betweenthe target variable

and the measured variable x₁, according to Equation 1.min[z,21 −x₁ ^(measured)]²  (1)

In certain embodiments, the AI module 102 may work to control thecertain variables or data points that are designated with a variabletype 0, which indicates an effectual relationship for the performance ofthe target variable

. For example, to solve the function defined in Equation 1, the AImodule 102 may determine a best fit function for the variables or datapoints that are designated with a variable type 0 (e.g., f(x₂, x₃, x₄)).

In any case, based on the model generated by the AI module 102, the AImodule 102 may determine adjustment operations for the motor or otherrelated components to control the target variable

with respect to the defined parameters. As such, the AI module 102 maysend the adjustment operations to the control/monitoring device 54 toimplement, thereby causing the operation of the industrial automationsystem 10 or a portion thereof to change.

By employing the AI module 102 in this fashion, the AI module 102 mayoptimize the target variable

by controlling the operation of various components that affect theperformance of target variable

. The control aspects may occur in real time and may include anticipatedchanges in the target variable

based on the performance of other parameters. In this way, the AI module102 may enable the industrial automation system 10 or a portion thereofto operate more efficiently, while avoiding potential issues based onthe predicted model of various target variables.

With the foregoing in mind, the presently disclosed embodiments mayenable the AI module 102 to automatically build a model without thepresence of a human expert in the work flow of the modeling, asdescribed above. In addition to modeling a function or target variable,the AI module may perform efficient analysis to determine operationalfunctions or algorithms that may be used to operate the industrialautomation equipment 56 more efficiently or solve a requested problem orcondition statement more effectively.

By way of example, FIG. 22 illustrates a flow chart of a method 360 forcontrolling the operations of an industrial automation device 20, theindustrial automation equipment 56, the industrial automation system 10,a portion of the industrial automation system 10, or the like using themodeling operations and a more accurate operational function. Like themethods described above, the method 360 may be performed by any suitablecomputing device in any suitable order.

Referring now to FIG. 22, at block 362, the AI module 102 may receive atarget variable that has been designated for modeling. The targetvariable may be identified via user input or via automaticidentification processes described herein. In one example, the targetvariable may include an increase in the “cost of control” (e.g. increasein tracking error for one or more variables being controlled, orincrease in the objective function in a model predictive controlformulation that may include the cost of movement of the manipulatedvariables).

At block 364, the AI module 102 may receive parameters for the targetvariable. The parameters may include the components described above withrespect to FIG. 21. As such, the parameters may include conditionstatements (e.g., minimum value, maximum value), an operationalfunction, or the like. At block 366, the AI module 102 may identify datapoints that influence the target variable with respect to the parametersreceived at block 362 based on the embodiments described above.Continuing the example described above, an automated modeling engine ofthe AI module 102 identifies which process variable(s) contribute to theincrease in the “cost of control.”

At block 368, the AI module 102 may model the data points identified atblock 366. The modeled data points may be designated as new targetvariables to control the target variable defined at block 362. Referringagain to the example above, the AI module 102 may, at block 368,automatically build a model of the variable(s) found to be the cause ofthe increase in the “cost of control.” In some embodiments, these newtarget variables may be referred to as root cause variables.

At block 370, the AI module 102 may determine a new function or adjustthe function defined in the parameters received at block 364 to controlthe target variable received at block 362 based on the model generatedat block 368. Continuing with the example presented herein, the AImodule 102 may adjust or augment the original objective function forcontrol with appropriate terms aimed at minimizing the variation in theroot cause variable(s). In some embodiments, the AI module 102 performsa root-cause analysis using automated modeling procedures describedearlier.

At block 372, the AI module 102 may adjust the operations of therespective industrial automation device 20, the respective industrialautomation equipment 56, and the respective industrial automation system10 based on the updated or new function determined at block 370. Thatis, the AI module 102 may employ this as an adaptive strategy to improvethe functional or conditional parameters used to control a particulardevice based on the ability of the AI module 102 to perform automatedmodeling with minimal or no reliance on a human expert. It should benoted that by employing the embodiments described herein that AI module102 may have the ability to manage the entire workflow (e.g., fromassessing that a certain improvement in a given target variable needs tohappen to meet the pre-defined operation targets, to identifying whatvariables can be manipulated to impact that said target variable, todetermining how the identified set of variables relate to the targetvariable, to proposing an adjustment to the manipulated variables).Additionally, the AI module 102 may determine messages, destinations forthe messages (e.g., operation roles/personnel the decisions/results), amethod for communication, and the like.

Triggering the Retraining of Model Target Variables

In certain embodiments, the AI module 102 may be tasked with modeling atarget variable that corresponds to data acquired by a sensor. Forexample, data point x₁ may correspond to the target variable

. In this example, data point x₁ may have a variable type 1 assigned toit, thereby indicating to the AI module 102 that the data point x₁ willbe modeled based on other data points (e.g., data points x₂ and x₃).Keeping this in mind, data points x₂ and x₃ may have a variable type 0assigned to it, thereby indicating to the AI module 102 that these datapoints are to be used to determine the target variable

. An example of the measured data points x₁, x₂, and x₃ and the targetvariable

is illustrated in FIG. 23.

Referring to FIG. 23, graph 380 may include sensor data for data pointsx₁, x₂, and x₃. The sensor data for data points x₁, x₂, and x₃ arepresented as lines 382, 384, and 386, respectively. Line 388, however,tracks the value of the modeled target variable

. As shown in the graph 380, the line 388 follows a similar path as theline 382. However, at time t₀ the line 388 and the line 382 start todiverge to different values. This divergence may correspond to an errorbetween the modeled target variable

and the actual target variable data recorded as data point x₁.

With the foregoing in mind, in some embodiments, the AI module 102 maytrain a model that characterizes the behavior of the modeled targetvariable

according to the data points x₂ and x₃. After training the model, the AImodule 102 may track the error between the target variable

and the measured data point x₁. In some embodiments, the model of thetarget variable

may not accurately represent the measured data point x₁ over time. Tobetter ensure that the AI module 102 is accurately determining thetarget variable

, the AI module 102 may monitor an error between the target variable

and the measured data point x₁. If the error increases above somethreshold, the AI module may stop calculating the target variable

and retrain the target variable

based on the newly acquired data points that are designated as variabletype 0. When retraining the model, different coefficients and/or othervariables may be used to more accurately represent the target variable

.

In some embodiments, the AI module 102 may employ a tiered system forretraining the model as presented in method 400 of FIG. 24. Forinstance, at block 402, the AI module 102 may receive data points fromthe I/O module 104 or other suitable device. The AI module 102 mayreceive the data points that correspond to the variable type 0 for aparticular target variable designated for modeling. The data points maycorrespond to sensor data, or other

At block 404, the AI module 102 may train a model for the targetvariable based on the data points received at block 402. In addition toreceiving the data points used to train the model, at block 406, the AImodule 102 may receive an additional data point from a sensor 16, suchthat the data point corresponds to the actual data of the modeled targetvariable. The additional data point(s) may be used to verify or checkthat the modeled target variable

is accurate.

At block 408, the AI module 102 may determine whether an error betweenthe target variable

and the measured data point x₁ is greater than a first threshold (e.g.,5%) and less than a second threshold (e.g., 10%). If the error is withinthis first range of thresholds, the AI module 102 may proceed to block410 and retrain the model using the original data points designated asvariable type 0. Alternatively, the AI module 102 may proceed to block412. It should be noted that, in some embodiments, the user may specifythe thresholds to the AI module 102. Alternatively, the AI module 102may determine the thresholds.

If the error is greater than the second threshold and less than a thirdthreshold (e.g., 15%), the AI module 102 may proceed to block 414 andanalyze the data points designated as variable type 0 to identify a newfunction that defines the behavior of the target variable

, as described above with respect to the method 360. Alternatively, theAI module 102 may proceed to block 416.

At block 416, the AI module 102 may determine if the error is greaterthan the third threshold (e.g., 15%). If the error is greater than thethird threshold, the AI module 102 may proceed to block 418 anddesignate other data points as variable type 0 and identify clusters andrelationships from anew. That is, the AI module 102 may receive acollection of available data points from the data backplane 106 andidentify clusters and relationships between data points and the targetvariable

, as described above with reference to FIGS. 7, 11, 13, 17, and thelike.

If, at block 416, the AI module 102 determines that the error is notgreater than the third threshold, the AI module 102 determines that theerror is less than the first threshold. In some embodiments, when theerror is less than the first threshold, the AI module 102 may considerthis error as an acceptable error. As such, the AI module 102 may returnblock 402 and continue to monitor the data points related to the targetvariable

.

By way of example, referring to the graph 380 of FIG. 23, between timet₀ and time t₁, the error or the difference between the target variable

and the corresponding sensor data or calculated data that corresponds tothe actual value for the target variable

may be acceptable to the AI module 102. Between time t₁ and time t₂, theAI module 102 may retrain the target variable

using the existing data points used to train the model for the targetvariable

, as described above in block 410, because the error is within a firstrange of values.

Between time t₁ and time t₂, the AI module 102 may determine that theerror is within a second range of values that correspond to identifyinga new function or adjustment to the function used to generate the modelof the target variable

, as described above with respect to block 414. In the same manner,between time t₂ and time t₃, the AI module 102 may determine that theerror is greater than some value that corresponds to generating a newmodel based on the unbiased analysis described above with respect toblock 418.

In another embodiment, the AI module 102 may use the prediction error toautomatically inform the operators of the causes of the increase inprediction error. In this case, the AI module 102 may designate themonitored error as a target variable (e.g. sets the variable type forprediction error to 1), and may spawn or execute a data-driven modelingexercise to build a model of the monitored error. It is possible thatthe outcome of this modeling exercise will reveal process variables thatare more responsible for increase in prediction error than other processvariable, and therefore suggest a set of potential root-causes for humanexpert's review.

By employing the tiered modeling operations described above, the AImodule 102 may efficiently maintain an accurate model of the targetvariable. That is, the AI module 102 may perform tiered retrainingoperations based on the discrepancy between the actual and modeledvalues. The tiered retraining operations may enable the AI module 102 toconserve computing resources and power when the error is relativelysmall. However, as the error increases, the AI module 102 may performmore rigorous or increased computations to ensure that the modeledtarget variable is accurate.

In another embodiment, the AI module 102 may predict the error for themodeled value based on the techniques described herein. The predictionof error can be used to provide advice on remaining useful lifetime foran asset. That is, the remaining useful lifetime is a useful input forpredictive maintenance applications, and, as such, has been the subjectof interest by both academia and industry. In some embodiments,determining the useful lifetime may involve: (a) an expert using anin-depth understanding of unit operation to determine how far from afailure a certain unit operation is; or (b) operation data collectedover the lifetime of a unit (e.g., up to and including the failure mode)for several units of the same type and a learning algorithm is used tolearn the signature of the failure (preferably enough in advance), suchthat the AI module 102 or the control/monitoring device 54 may benotified of an impending likelihood of failure.

By employing the techniques described in the present disclosure, the AImodule 102 may enable a self-driven AI engine to use the ability toextract fundamental physical relationship from the normal operation datato establish a baseline for deciding the remaining useful lifetime. Morespecifically, the AI engine 102 may use the increase in modeling erroras an indication of the loss of the remaining useful lifetime. Thiserror along with the rate at which this error increase changes may offera measure of remaining useful life for the equipment. In the absence ofdomain expertise to guide the AI engine to target a key physicalsignature of unit operation, the AI engine may use a causality analysisconducted for example as an independent process on the AI module 102 todetermine the feature or target variable to be modeled automatically todetermine the cause for the respective issue.

While only certain features of the embodiments described herein havebeen illustrated and described, many modifications and changes willoccur to those skilled in the art. It is, therefore, to be understoodthat the appended claims are intended to cover all such modificationsand changes as fall within the true spirit of the embodiments describedherein.

The invention claimed is:
 1. An industrial automation system,comprising: an automation device; a plurality of sensors configured tomonitor a plurality of properties associated with the automation device,wherein each sensor of the plurality of sensors is configured to acquirea set of data points associated with a respective property of theplurality of properties; and a control system communicatively coupled tothe plurality of sensors, wherein the control system comprises a firstmodule of a plurality of modules configured to: receive a plurality ofsets of data points acquired by the plurality of sensors; identify afirst portion of the plurality of sets of data points based on a firstset of changes related to the first portion of the plurality of sets ofdata points, wherein the first set of changes corresponds to a secondset of changes related to a second portion of the plurality of sets ofdata different from the first portion of the plurality of sets of data;determine a modeled value of one of the plurality of sets of data pointsbased on the first portion of the plurality of sets of data pointsacquired by a first portion of the plurality of sensors and a model,wherein the modeled value is representative of the one of the pluralityof sets of data points as acquired by a second portion of the pluralityof sensors, wherein the first portion of the plurality of sensors andthe second portion of the plurality of sensors are different; determinewhether an error between the modeled value and the one of the pluralityof sets of data points is greater than a first threshold; and retrainthe model associated with the one of the plurality of sets of datapoints based on the first portion of the plurality of sets of datapoints in response to the error being greater than the first threshold.2. The industrial automation system of claim 1, wherein the first moduleis configured to retrain the model associated with the one of theplurality of sets of data points based on the first portion of theplurality of sets of data points in response to the error being greaterthan the first threshold and less than a second threshold.
 3. Theindustrial automation system of claim 1, wherein the first module isconfigured to adjust a function employed by the model based on the firstportion of the plurality of sets of data points in response to the errorbeing greater than a second threshold.
 4. The industrial automationsystem of claim 1, wherein the first module is configured to adjust afunction employed by the model based on the first portion of theplurality of sets of data points in response to the error being greaterthan a second threshold and less than a third threshold.
 5. Theindustrial automation system of claim 4, wherein the first module isconfigured to recreate the model based on the plurality of sets of datapoints in response to the error being greater than the third threshold.6. The industrial automation system of claim 1, wherein the first moduleis configured to recreate the model based on the plurality of sets ofdata points in response to the error being greater than a secondthreshold.
 7. A method for operating an industrial automation system,comprising: receiving, via a first module of a plurality of modules in acontrol system, an indication that an error between a measurementassociated with a target variable acquired by a first portion of aplurality of sensors of the industrial automation system and a modeledvalue for the target variable, wherein the modeled value isrepresentative of one of a first portion of a plurality of sets of datapoints as acquired by a second portion of the plurality of sensors;determining, via the first module, whether the error is within a firstrange of values; identifying, via the first module, a second portion ofthe plurality of sets of data points based on a first set of changesrelated to the first portion of the plurality of sets of data points,wherein the first set of changes corresponds to a second set of changesrelated to the second portion of the plurality of sets of data differentfrom the first portion of the plurality of sets of data; and retraining,via the first module, a model used to generate the modeled value for thetarget variable based on the second portion of a plurality of sets ofdata points acquired via the second portion of the plurality of sensorsdisposed in the industrial automation system in response to the errorbeing within the first range of values, wherein the first portion of theplurality of sensors and the second portion of the plurality of sensorsare different.
 8. The method of claim 7, wherein retraining the modelcomprises: receiving one or more parameters associated with the targetvariable, wherein the model is configured to control the target variablewith respect to the one or more parameters; and modifying one or morefunctions associated with the model in response to the error beingwithin a second range of values different from the first range ofvalues.
 9. The method of claim 7, wherein each data point of the secondportion of the plurality of sets of data points is characterized as afirst type of data point for modeling the target variable.
 10. Themethod of claim 7, comprising recreating the model based on theplurality of sets of data points in response to the error being greaterthan a threshold.
 11. The method of claim 10, comprising modifying oneor more functions associated with the model in response to the errorbeing: within a second range of values greater from the first range ofvalues; and less than the threshold.
 12. The method of claim 10, whereinrecreating the model comprises determining one or more relationshipsbetween each data point of the plurality of sets of data points.
 13. Themethod of claim 7, wherein the model comprises a best fit function basedon the second portion of a plurality of sets of data points.
 14. Anon-transitory computer-readable medium comprising computer-executableinstructions that, when executed, are configured to cause a processorto: receive a plurality of sets of data points acquired by a pluralityof sensors; identify a first portion of the plurality of sets of datapoints based on a first set of changes related to the first portion ofthe plurality of sets of data points, wherein the first set of changescorresponds to a second set of changes related to a second portion ofthe plurality of sets of data different from the first portion of theplurality of sets of data; determine a modeled value of one of theplurality of sets of data points based on the first portion of theplurality of sets of data points acquired by a first portion of theplurality of sensors and a model, wherein the modeled value isrepresentative of the one of the second portion of the plurality of setsof data points as acquired by a second portion of the plurality ofsensors, wherein the first portion of the plurality of sensors and thesecond portion of the plurality of sensors are different; determinewhether an error between the modeled value and the one of the pluralityof sets of data points is greater than a first threshold; and retrainthe model associated with the one of the plurality of sets of datapoints based on the first portion of the plurality of sets of datapoints in response to the error being greater than the first threshold.15. The non-transitory computer-readable medium of claim 14, wherein thecomputer-executable instructions are configured to cause the processorto retrain the model associated with the one of the plurality of sets ofdata points based on the first portion of the plurality of sets of datapoints in response to the error being greater than the first thresholdand less than a second threshold.
 16. The non-transitorycomputer-readable medium of claim 14, wherein the computer-executableinstructions are configured to cause the processor to adjust a functionemployed by the model based on the first portion of the plurality ofsets of data points in response to the error being greater than a secondthreshold.
 17. The non-transitory computer-readable medium of claim 14,wherein the computer-executable instructions are configured to cause theprocessor to adjust a function employed by the model based on the firstportion of the plurality of sets of data points in response to the errorbeing greater than a second threshold and less than a third threshold.18. The non-transitory computer-readable medium of claim 17, wherein thecomputer-executable instructions are configured to cause the processorto recreate the model based on the plurality of sets of data points inresponse to the error being greater than the third threshold.
 19. Thenon-transitory computer-readable medium of claim 14, wherein thecomputer-executable instructions are configured to cause the processorto recreate the model based on the plurality of sets of data points inresponse to the error being greater than a second threshold.
 20. Theindustrial automation system of claim 1, wherein the plurality of setsof data points acquired by the plurality of sensors comprises raw sensordata.